If LLMs can write abstracts, what's our job? 

URL Arguments in API Calls Can Cause

Intermittent Temporal Violations

While Replaying Archived Web Pages 

Michael L. Nelson

2026-05-26

Just over two months ago, I was at the ​Information Stewardship Forum 2026 at the Internet Archive, where I was fortunate enough to present a lightning talk about making copies of copies, entitled "The Disintegration Loops: Generational Loss in Web Archives".  During one of the breaks, Mark Graham asked Sawood Alam to take a look at a problem that had stumped the Wayback Machine support team.  I was sitting next to Sawood, and knowing my love for web archiving investigations, Mark invited me to take a look too.  The original inquiry:

Hi, everyone! Got a concerning report from a patron alleging that WBM "URLs were intermittently displaying the current version of the website instead of the archived version." The URLs in question are:

• https://web.archive.org/web/20240222221058/https://www.victoriassecret.com/us/site-terms-and-notices 

• https://web.archive.org/web/20241228224626/https://www.victoriassecret.com/us/site-terms-and-notices 

• https://web.archive.org/web/20250531013827/https://www.victoriassecret.com/us/rewards/terms-and-conditions 

A quick check shows that when replaying these URLs, the content does resemble what is on the live web. For example, the text shown on the page references 2025 and 2026 updates, even though the captures are from 2024 - 2025. I've attached a screenshot of the 2025 capture appearing to show live web content as well as a printout/capture the patron provided of the same URL appearing to show the "actual" archive.

Sawood and I discovered that the problem is not that these URLs are sometimes displaying the live web (or at least not directly). The problem is that this seemingly simple "Terms of Use" page is unnecessarily complex, with the boilerplate legal text included via an API call.  The JavaScript that makes the call includes a number of superfluous URL arguments, including "screenWidth" and "screenHeight", and probably are appended to all API calls "just in case they are needed" (presumably the "Terms of Use" do not actually vary based on the size of the browser).  Thus, depending on the size of your browser, the legal text included in the page is potentially archived at different times, sometimes resulting in a temporal violation: a replay of an archived web page with subresources in a combination that did not exist at the time the top level page was archived.  

Although there are potentially a countably infinite number of archived "Terms of Use" pages, for the examples above there are two semantically interesting versions: one is marked (near the top, left-hand side) "Last Updated: January 18, 2024" and the other is marked "Last Updated: September 22, 2025".  Taking these "Last Updated" strings at face value, we would not expect the three URLs above (archived at "20240222221058" (February 22, 2024), "20241228224626" (December 28, 2024), and "20250531013827" (May 31, 2025)) to display "Last Updated: September 22, 2025". But sometimes they do – and sometimes they don't – and which archived version you get depends on the size of your browser.  

First, as of the time of this writing, the live web still has the "Last Updated: September 22, 2025" version:

https://www.victoriassecret.com/us/site-terms-and-notices

What appears to be a relatively simple HTML page is unnecessarily complex, with nearly 200 subresources. The figure below shows the relevant portion of the call stack: the HTML page calls the cheekily named JavaScript "brastrap.js", which in turn calls the API at "api.victoriassecret.com".  

https://api.victoriassecret.com/categories/v15/page?...

For me, right now, the full live web URL is (emphasis added):

https://api.victoriassecret.com/categories/v15/page?categoryId=4b1ed4b3-5965-4a4d-a3d5-1e5ad379445a&brand=vs&isPersonalized=true&activeCountry=US&platform=mobile&deviceType=phone&platformType=ios&perzConsent=true&cid=&tntId=&screenWidth=701&screenHeight=605

Guessing at the URL arguments: 

• categoryId=4b1ed4b3-5965-4a4d-a3d5-1e5ad379445a

• I guess this hash identifies the "Terms of Use" page?

• brand=vs

• "vs" = Victoria's Secret?  I believe the parent company operates several affiliated brands, and perhaps the API serves all of them.   

• isPersonalized=true

• should this be "false"? – I don't have an account here

• activeCountry=US

• I'm definitely coming from a US IP address 

• platform=mobile, deviceType=phone, platformType=ios

• none of these are accurate; I'm on a Mac Air laptop. 

• perzConsent=true

• looks like a GDPR-related argument 

• cid=, tntId=

• tracking arguments (currently null)

• screenWidth=701, screenHeight=605

• these are the current dimensions of the active window in my Chrome browser 

It's the last two arguments, "screenWidth" and "screenHeight", that cause the intermittent behavior the original users noticed.  

First, let's consider the page archived on February 22, 2024 ("20240222221058"), which clearly shows the "Last Updated: September 22, 2025" string:

https://web.archive.org/web/20240222221058/https://www.victoriassecret.com/us/site-terms-and-notices

And since the live web still has "Last Updated: September 22, 2025", this is what caused people to think they were getting a live web version (more on that in a bit).  First of all, the Wayback Machine's "About this capture" link does not help; it shows only some of the subresources (improving its function is a task for another time):

"About this capture" lists only some of the subresources, and not the problematic api.victoriassecret.com page. 

Sawood discovered the API URL first. It's well-obfuscated, so it's not a surprise that tech support staff did not find it immediately.  We were sitting side by side, each using our own laptops, and he's much smarter than me and he's always going to win that race. But I noticed that for me, the page seemed to be saved right then, just a minute or two before, whereas he saw that it was archived a few days before (it was then March 19, 2026).  That was odd, but the next session started and I had to stop. 

The 2024 archived version of the page uses a "/v12/" version of the API endpoint (note: this is a common but wrong way to version an API), but it's similar to the 2026 live web example above:

https://web.archive.org/web/20260319160602/https://api.victoriassecret.com/categories/v12/page?categoryId=43b79880-5d66-4747-a553-65aeb3bd1876&brand=vs&isPersonalized=true&activeCountry=US&platform=web&deviceType=&platformType=&cid=&perzConsent=true&tntId=&screenWidth=508&screenHeight=593

In particular, the "/v12/" endpoint remains functional, even though the live web HTML & brastrap.js access the "/v15/" version.  Checking the Wayback Machine directly confirmed that this was indeed the first time that URL had been archived:

https://web.archive.org/web/20260401000000*/https://api.victoriassecret.com/categories/v12/page?categoryId=43b79880-5d66-4747-a553-65aeb3bd1876&brand=vs&isPersonalized=true&activeCountry=US&platform=web&deviceType=&platformType=&cid=&perzConsent=true&tntId=&screenWidth=508&screenHeight=593

Although Sawood found the problem URL, and we confirmed it was archived in March, 2026 (and thus displayed the "Last Updated: September 22, 2025" string), it bothered me that he had an earlier archival time than I did (March 14, 2026 vs. March 19, 2026).  After the next session ended, I returned to this problem.  I changed the size of my browser, and was able to force another new archived version (reproduced on March 22, 2026 below):

The highlighted text shows: 

https://web.archive.org/save/_embed/https://api.victoriassecret.com/categories/v12/page?categoryId=43b79880-5d66-4747-a553-65aeb3bd1876&brand=vs&isPersonalized=true&activeCountry=US&platform=web&deviceType=&platformType=&cid=&perzConsent=true&tntId=&screenWidth=565&screenHeight=605 

Although it's beyond the scope of this post, the Wayback Machine's Save Page Now has a "/save/_embed/" API that allows the Wayback Machine to "patch" the archive with missing URLs from the live web.  In this case, the version of the API response ending with "&screenWidth=565&screenHeight=605" was "missing" from the Wayback Machine, so it patched the archive from the live web, which still displays the "Last Updated: September 22, 2025" string, despite the main HTML page being archived in February, 2024.  So in essence, the Wayback Machine was displaying the live web version, after it was immediately saved to Wayback Machine.  Presumably the "Terms of Use" page changes slowly, but this behavior would be more noticeable if the "Last Updated" string was updated, say, every minute. 

A call to the CDX API confirmed that there were a variety of screenWidth and screenHeight combinations archived (horizontally scroll to the right in the gist below to see the combinations):

In fact, by inspection, there are at least two chances to get the wrong version. If your screen size is "screenWidth=1600&screenHeight=1000", you will get a version of the page that has the string "Last Updated: February 7, 2023", a temporal violation reaching into the past instead of the previously described version that is a temporal violation from the future.  A screen size of "screenWidth=1400&screenHeight=900" will produce the right result ("Last Updated: January 18, 2024"), and a screen size of "screenWidth=1440&screenHeight=900" will produce a different wrong result ("Last Updated: September 22, 2025"). And as shown above, a screenWidth and screenHeight combination not already archived will cause the Wayback Machine to be patched from the live web.  Furthermore, if/when the "/v12/" live web API endpoint is deprecated, then unarchived size combinations will just cause the page replay to silently fail, and most people won't understand why.   

In summary, this seemingly simple "Terms of Use" page is really quite challenging in practice:

• The API call is not easily discovered, and the "About this capture" service does not show the API URL (and many of the other nearly 200 URLs of subresources in this page).

• The API has a raft of (arguably) unnecessary URL arguments that do not change the response and cause the Wayback Machine to patch the archive from the live web. 

• Because the temporally violative subresource is JSON and not, say, a JPEG, one can't simply right-click on the subresource and inspect when it was archived. 

We've encountered synchronization problems with HTML and JSON before (e.g., "Right HTML, Wrong JSON" (JCDL 2023), "Challenges in replaying archived Twitter pages" (IJDL 2024)), but the implementation complexity found in news outlets and social media was to be expected: the advanced UI features that make these sites engaging (e.g., auto-updating, infinite scroll, embedded media, personalized content) are the same features that make archival replay difficult. Without the "Last Updated: …" string, the problem would have been much harder to notice and diagnose. The seemingly intermittent nature, where you'd get a temporally coherent replay only if your browser was the same size as the previously archived responses, made the investigation especially challenging. 

Who pays attention to their browser's exact width and height? In this case, they were the keys to solving this puzzle.

–Michael 

Mark Graham welcoming attendees

My lightning talk

Me in front of the Internet Archive

Dr. Sawood Alam, me, Dr. Jian Wu

Cadey

What is an operating system, really?

Aoi

I mean, isn't it obvious? It's something like FreeBSD or Fedora that has a kernel, userspace, graphics stack, core set of programs, and everything else you need to be able to use a computer. Is this a trick question?

Numa

Well it depends, is the Nintendo Switch OS an operating system? It doesn't have a shell in the same way FreeBSD does. Is SEL4 an OS? It doesn't ship with core utilities. Is Linux an OS? Is Windows an OS?

Aoi

Oh gods here we go again…

The definition of an operating system gets really fuzzy when you start looking at the edges of it, but let's say that an operating system is any part of a computer system that doesn't involve pure math. When you print to the screen, render 3d graphics, connect to the internet, and write to files your code calls into the underlying system to do that work. These system calls are defined by your operating system and are exposed as functions*.

Mara

Okay they're not actually functions, but they quack enough like functions that you can treat them like functions and not have to worry about the details too much.

System calls are injected into each operating system process via a process kinda like how you inject dependencies into your applications for database sessions or object storage operations.

Bashing your head into the wall

A while ago a new JavaScript package got into the meme sphere at work: just-bash. It's a sandboxed environment with a shell interpreter that was originally intended for use with AI agents after its author observed that AI agents know how to use a tool called bash a lot better than a tool called search_documentation. This is backed by a "fake" shell with "fake" core utilities (cat, ls, etc, hereinafter coreutils) so that when an agent decides to rm -rf /, nothing important actually leaves the room. One of my coworkers made @tigrisdata/agent-shell on top of this that uses Tigris as its storage layer.

This is great for people in the JavaScript ecosystem, but I am not mainly a JavaScript developer. I really wanted to play with it so I started thinking what it would take to have something like this in Go. mvdan's shell package makes this a heck of a lot easier, meaning that this "fake" shell would be powered by a real shell instead of either porting half of bash to JavaScript or making up hopefully-compatible behaviour.

After a bunch of thought, hacking, and a spot of vibe coding while I did some Dawntrail extreme mount farms, I ended up with Kefka, a "fake" shell with coreutils implementations that lets you put your programs in clown jail. This package lets you add a sandboxed-in-userspace shell to your existing projects without shelling out to the actual implementations of coreutils on your machine.

Mara

The name is inspired from Kefka Palazzo, the final boss of Final Fantasy VI. Need to chain uncontrollable demons? Use the power of a mad god driven to the brink of insanity with raw access to magic! What could possibly go wrong!

So I did that

So after some thought, I came up with this interface for the "commands" to use: Execer. This takes process context and passes it as an argument to a function named Exec. Exec then does whatever the process needs it to (list files, write to stdout, etc.) and returns an error if things went wrong and no error if things didn't.

type ExecContext struct {
        	Stdin          io.Reader
        	Stdout, Stderr io.Writer
        	Dir            string
        	Environ        expand.Environ
        	FS             billy.Filesystem
        	// Runner is the active shell runner. Commands that need to dispatch a
        	// child command (for example, `time CMD`) should call Runner.Subshell()
        	// and re-enter the shell so the call goes through the same exec handler
        	// chain instead of poking at the registry directly. May be nil in
        	// embedders or tests that have not wired up a runner.
        	Runner *interp.Runner
        }
        
        type Execer interface {
        	Exec(ctx context.Context, ec *ExecContext, args []string) error
        }
        

This is where I started vibe coding things, mostly via a skill that ports a just-bash command to the Execer interface and filesystem in Go. just-bash itself looks vibe coded from help output and manpages; I tried to go further and stay POSIX compatible, down to matching flag syntax (and in some cases output formats). If your muscle memory fails you, it's a bug in my book.

Aoi

If I recall, some POSIX utilities like false aren't usable as Go identifiers, how did you handle package names for that?

Cadey

By naming them things like falsecmd:

package falsecmd
        
        // ...
        
        type Impl struct{}
        
        func (Impl) Exec(ctx context.Context, ec *command.ExecContext, args []string) error {
        	return interp.ExitStatus(1)
        }
        

Honestly the implementations of true and false are my favourite part of this implementation. Here's the implementation of true:

package truecmd
        
        // ...
        
        type Impl struct{}
        
        func (Impl) Exec(ctx context.Context, ec *command.ExecContext, args []string) error {
        	return nil
        }
        

This is a fully POSIX compliant implementation of true! Here's the relevant part of the spec if you don't believe me:

true - return true value

SYNOPSIS

true

OPTIONS

None.

OPERANDS

None.

Really, check out the POSIX spec for true. It's trivial to implement, here's a oneliner to implement it in Linux:

touch ./true && chmod +x ./true
        

I made an operating system*

This is basically an operating system: it provides interfaces for programs (well, in this case functions) to get input from a user, send output to a user, interact with a filesystem, and more. Eventually I want to add networking via a network stack on ExecContext, probably with tsnet or wireguard-go's netstack package for the user-level side. Maybe there's room for adding CEL based network filters there too.

Porting applications with WebAssembly

Once I got basic coreutils working, I thought it would be fun to get Python, jq, and ripgrep working. From previous experimentation back in the strawberry era of AI, I had already gotten Python running in WebAssembly via wazero. This used the stdlib io/fs#FS interface to allow me to inject virtual filesystems into the WebAssembly context. I used this to isolate my chatbot's filesystem state so that it (hopefully) wasn't able to delete anything important by accident.

io/fs#FS has methods for the important stuff, and runtime interface assertions let you bridge the gap for things like writes. But it was really designed for embedded filesystems, and writes get hairy fast once you're talking to object storage or anything that isn't a tree of bytes on disk.

At some point I hit a wall and had to switch from io/fs#FS to billy, another filesystem interface that I think predates the standard library one. This gives you a bunch more methods that map a lot closer to filesystem semantics in ways that coreutils crave. The interface was also mostly compatible with io/fs#FS so most of the hard part was really changing out the type and then chasing down compiler errors until I found enough of a pattern to have Opus automate the rest of it.

From there it was a matter of adapting billy's filesystem to wazero's experimental sys interface. Mostly glue code, except where I had to translate Go errors into POSIX errno values. I had to read both the POSIX spec, the WASI spec, and the wazero source to figure out how to map errors between the two worlds. I think I'm at least 95% correct, which is likely within the margin of porting error.

Adapting that codeinterpreter/python library to the new interface was mostly straightforward, and I ended up with a flow like this:

// from https://tangled.org/xeiaso.net/kefka/blob/main/command/internal/python3/python3.go
        
        func (Impl) Exec(ctx context.Context, ec *command.ExecContext, args []string) error {
        	fsConfig := wazero.NewFSConfig().
        		(sysfs.FSConfig).
        		WithSysFSMount(billyfs.New(ec.FS), "/")
        
        	config := wazero.NewModuleConfig().
        		// Pipe ExecContext stdio
        		WithStdin(ec.Stdin).WithStdout(ec.Stdout).WithStderr(ec.Stderr).
        		// Pipe argv
        		WithArgs(append([]string{"python3"}, args...)...).
        		WithName("python3").
        		// Pipe filesystem
        		WithFSConfig(fsConfig).
        		// Pipe system time
        		WithSysNanosleep().WithSysNanotime().WithSysWalltime()
        
        	mod, err := runtime.InstantiateModule(ctx, compiled, config)
        	if err != nil {
        		// Fit the square peg into the round hole
        		if exitErr, ok := errors.AsType[*wsys.ExitError](err); ok {
        			if code := exitErr.ExitCode(); code != 0 {
        				return interp.ExitStatus(uint8(code))
        			}
        			return nil
        		}
        		return err
        	}
        	return mod.Close(ctx)
        }
        

Mara

See? The dependencies such as stdin, stdout, and stderr get injected into the WebAssembly guest. Wazero also makes you inject the implementation of time for boring reasons involving deterministic computing, but I'm sure you can see the ways things hook in. This basic dependency injection flow is how things like the linuxulator in FreeBSD or the old version of the Windows Subsystem for Linux work (WSL1 before it was made into a Linux VM with WSL2). The table of system calls and filesystem context is effectively an argument to the process.

Same trick got me ripgrep and jq. jq was annoying because wasi-sdk doesn't love jq's (ab)use of cmake; however 30 or so minutes of tweaking compiler flags got me a binary that works enough.

I could see it being pretty easy to port over arbitrary programs to Kefka using WebAssembly like this. There's just one small problem: WASI preview 0.1 doesn't allow you to open arbitrary network sockets. This has been a huge pain in practice (it means you can't do HTTP requests, database connections, or other common internet things from inside the WASM sandbox) and future work probably would include adapting wazero to use wasix instead of WASI 0.1.

Using filesystems that don't exist

OK, that handles filesystems that (arguably) exist, like the btrfs volume on my dev box. What about filesystems that don't? For the sake of argument, let's say you want this fake shell to interact with object storage as its main filesystem. At some level all you need to do is adapt the billy interface to object storage using something like storage-go.

Cadey

Disclaimer, I work at Tigris and developed this library for them. It's basically the S3 client with more methods to handle additional Tigris features like forks and snapshots. I'll be writing more about it soon.

After finding a basic implementation of an S3 -> Billy adapter, I vendored it into the Kefka repo and swapped out the "real" filesystem in cmd/kefka for an s3fs implementation pointed at a sample Tigris bucket. From there it was down to an iterative process of running commands, finding feature gaps when errors showed up, implementing them, fuzzing, and making sure things work mostly the same against Tigris as they do against a local filesystem.

WASI is cursed: it has no process-level "current working directory," which most programs assume exists. You patch around it by passing a CWD envvar, or just use absolute paths. I haven't hit anything broken in casual use, but expect rough edges. Here be dragons and this code may be known by the state of California to cause cancer.

Why does it have to use the command line?

Once everything got working with s3fs and a local shell, I wondered how hard it would be to make this work as an SSH server using the github.com/gliderlabs/ssh package. Hooking things up was pretty easy:

func HandleSSH(sess ssh.Session) error {
          // Convenience variables for SSH session values
          var stdout io.Writer = sess
          var stderr io.Writer = sess.Stderr()
          var stdin  io.Reader = sess
          ctx := sess.Context() // cancelled when the user disconnects
        
          // Kefka command registry with coreutils/python/jq/etc
          commands := registry.New()
          coreutils.Register(commands)
          wasmprog.Register(commands)
        
          // Base envvars for all programs, needed by POSIX
          env := expand.ListEnviron(
            "HOME=/",
            "PWD=/",
            "IFS=\n",
            "HOSTNAME=localhost",
            "USER="+sess.User(),
            // not strictly required, but just-bash sets it
            "MACHTYPE=x86_64-pc-linux-gnu",
          )
        
          // Create shell engine
          sh, err := interp.New(
            // Set the "interactive" flag so the shell expands aliases
            interp.Interactive(true),
            // Forward our envvars
            interp.Env(env),
            // Wire up stdio
            interp.StdIO(stdin, stdout, stderr),
            // Change the shell exec handler such that it's constrained to the
            // Kefka registry.
            //
            // Strictly speaking you don't have to do this, but if you don't
            // then any time the registry doesn't have a command
            // implementation, interp falls back to its default ExecHandler that
            // executes the command as a subprocess. This is almost certainly
            // not what you want.
            interp.ExecHandlers(constrainToRegistry(commands)),
            // Wire up per-command pwd state to the filesystem implementation
            interp.CallHandler(billysh.CallHandler(commands, fsys, stdout, stderr)),
            // Handle shell-level filesystem I/O (redirects, glob expansion, etc)
            interp.StatHandler(billysh.FsysStatHandler(commands, fsys)),
            interp.FsysOpenHandler(billysh.FsysOpenHandler(commands, fsys)),
            interp.ReadDirHandler2(billysh.FsysReadDirHandler(commands, fsys)),
          )
        
          // Read shell commands
          parser := syntax.NewParser()
          fmt.Fprintf(stdout, "$ ")
        
          // Split input into commands
          for stmts, err := range parser.InteractiveSeq(stdin) {
            if err != nil {
              return err
            }
        
            if parser.Incomplete() {
              fmt.Fprintf(stdout, "> ")
              continue
            }
        
            for _, stmt := range stmts {
              err := sh.Run(ctx, stmt)
              if sh.Exited() {
                return err
              }
            }
        
            // Show prompt
            fmt.Fprintf(stdout, "$ ")
          }
        
          return nil
        }
        

The real handler is much messier because Python's REPL needs careful buffering, Ctrl-C has to actually cancel things, and pty wiring is its own can of cans of worms. None of that shows up if it's working. Tab completion and readline polish are easy enough; I'll let you wire those up as an exercise for the reader.

If you want to try it today, you can ssh into sophia.xeiaso.net:

$ ssh sophia.xeiaso.net
        

You'll get an isolated sandbox in your own bucket fork/branch. Every ls is a ListObjectsV2 against the bucket. Every qjs or python3 runs WebAssembly on the server, wired to that same bucket.

$ cat ./samples/hello.js
        console.log("Hello, world!");
        $ qjs ./samples/hello.js
        Hello, world!
        

The demo bucket is seeded with examples. You'll probably have to poke around to find everything. Worst case, run help.

Cadey

I should really hook up session recording to this.

I want more experimental WebAssembly hacks like this to exist. I'll keep poking at it.

Put your programs in clown jail

With some effort, yeet could use Kefka's shell utilities to run Anubis builds on Windows; and if management ever makes you babysit AI agents, clown jail is a decent answer.

The code lives on Tangled. I'm wiring it into an agent harness so I can automate small tools against a local model (I'm loving Qwen3-36B-A3B).

There's a sister post on the Tigris blog that goes deeper into the AI-agent angle and the porting work using Claude Code. If you want, you can check it out here:

Tigris DataGive your agents disposable environments in GoKefka is a userspace shell sandbox in Go that gives every AI agent its own copy-on-write Tigris bucket fork plus Python, jq, and ripgrep via WebAssembly.

In brief

Academic librarians and others often engage with media literacy instruction by promoting fact-checking strategies, such as lateral reading or Mike Caulfield’s SIFT. Evidence shows that these strategies are valuable and can be effective, but they all ultimately rely on individual students to use willpower to overcome cognitive habits, biases, strong parasocial relationships with content creators, the power of algorithms, and other challenges to fact-checking content in the moment. This paper offers an alternative approach that instead encourages librarians to support students in intentionally redesigning their information environments to improve the quality of information that they encounter in the first place.

By Mandi Goodsett

“The task of breaking a bad habit is like uprooting a powerful oak within us. And the task of building a good habit is like cultivating a delicate flower one day at a time.” – James Clear

In a 2024 study conducted by the News Literacy Project, the organization found that 80% of the teen participants believed that journalists fail to produce more impartial information than other online content creators, and 69% said that news organizations intentionally make their content biased to advance a particular viewpoint. When the News Literacy Project followed up with these young adults a year later, they found that most of them believe that trustworthy, unbiased news is rare or maybe doesn’t even exist (2025).

Pew Research found, through a series of focus groups, that Americans don’t always agree on what constitutes a “journalist” or “news media,” and young adults are more likely than older adults to call “new media” platforms hosts, such as podcasters and social media creators, “journalists” (Eddy et al., 2025). Overall, younger participants were less likely than older adults to even care whether the news they consume comes from a journalist. The investigation found that Americans are concerned that, besides maybe a few reliable ones, journalists are concerned with “clicks, eyeballs, money, things like that, and they don’t necessarily mind tweaking the truth to suit their audience or their advertisers” (quoted in Pew Research, 2025). 

These statistics are significant because cynicism about standards-based news and other traditionally authoritative institutions has many negative impacts. First, news cynicism can lead to news disengagement, which pushes information consumers to less reliable platforms (Ahmed et al., 2025; Fletcher, et al., 2024; Mont’Alverne, 2022) and contributes to erosion of trust more broadly in institutions like voting (Park, et al., 2025; Raffio, 2025). When people disengage, news sources themselves are threatened by obsolescence, and this threatens their role as a watchdog and a keystone of democratic societies (Haider & Sundin, 2022). News cynicism makes it difficult for accurate information to reach people and, paradoxically, makes people more vulnerable to misinformation (Ahmed et al., 2025; Hasell & Halversen, 2024). Individuals may feel anxious, depressed, and helpless about their world, leading to a spiral of disengagement (Hasell & Halversen, 2024). News cynicism also fuels societal division and threatens democracy (Cappella & Jamieson, 1996; Valgarðsson et al., 2025). Widespread distrust in institutions such as the government, science, public authorities, and the press is a risk to media literacy, democracy, civil discourse, and our sense of agency.

Academic Librarians and Media Literacy Instruction

One strategy for helping students and others improve their media consumption is to teach them media literacy skills. Media literacy is generally thought to be the ability to access, evaluate, analyze, and create media messages (Aufderheide, 1993), although definitions vary considerably between researchers and practitioners (Fleming, 2014; Hobbs, 1998). Media literate individuals have the skills to identify media sources and messages that are unreliable, and, perhaps more importantly, craft an overall media diet that is more likely to consist of reliable information.

Academic librarians are interested in and possess relevant expertise to teach students media literacy skills that are relevant in academic and non-academic settings. Many librarians have explored tactics for teaching students source evaluation skills that move beyond the CRAAP test (Currency, Relevance, Authority, Accuracy, Purpose), such as the SIFT method (Stop, Investigate, Find, Trace), created by Mike Caulfield (2019), or lateral reading, popularized by the Civic Online Reasoning organization (Digital Inquiry Group, n.d.). Caulfield’s SIFT method provides a more up-to-date approach to source evaluation by offering strategies that are more efficient, straightforward, and applicable in a wide variety of contemporary information settings (Bull, 2021). “Lateral reading,” which is a key component of SIFT, involves leaving the source that is being evaluated and opening new browser tabs to investigate what other Internet sources report about the site and its claims (Wineburg & McGrew, 2019). Research has shown that the SIFT method and lateral reading results in more accurate student source evaluation (Bobkowski & Younger, 2020; Breakstone et al., 2021; Brodsky et al., 2021). These techniques reflect a better understanding of the modern online information environment than simplistic checklist strategies. However, they still expect students to avoid misinformation through careful self-control and self-monitoring.

Misinformation is an interdisciplinary problem with significant complexities. As Sullivan has argued, librarians have historically focused on media literacy instruction strategies that neglect the psychology of how people interact with information, and the field of library and information sciences is somewhat siloed in its exploration of source evaluation instruction (2019). For example, heuristics, systems thinking, mental models, and cognitive biases all play a role in how and why people adopt misinformed beliefs. Emotions also influence the ways that individuals evaluate information (Hewitt, 2023; Hicks & Lloyd, 2021), yet they play a minor role in most library source evaluation instructional strategies. Academic librarians may have a role in combatting misinformation, but we should proceed, as much as possible, guided by research conducted across disciplines (Saunders, 2025). As an example, academic librarians have often focused their source evaluation teaching on investigation strategies and fact-checking skills. These skills are very important, and we shouldn’t abandon them. But there are many reasons, informed by research outside of Library and Information Science (LIS), why reactive strategies that rely on individual willpower are destined to be difficult to maintain. 

Challenges of Fact-Checking and Other Traditional Source Evaluation Techniques

Evidence shows that, globally, trust in institutions is decreasing, including in democratic societies (Kavanagh & Rich, 2018; Gil de Zúñiga & Diehl, 2019). The consequences of this could be severe, as many scholars posit that trust in institutions is an important pillar of democracy (Haider & Sundin, 2022). There are also a number of well-studied examples of how bad actors can sow doubt in institutions, such as academia, to achieve their own ends (Haider & Sundin, 2022). This has played out in the case of the tobacco industry and fossil fuel companies; in both cases, the science is clear, but raising uncertainty can be enough to sway consumers to take actions that are not in their best interests (Oreskes & Conway, 2010). All of this said, when society’s institutions become corrupt or unreliable, or when institutions are systematically unfair to one’s group or identity, distrust in institutions is often justified (Haider & Sundin, 2022). So while dismissing institutionally-backed information in favor of persuasive individuals is risky, confidently pointing to institutions as always trustworthy is also unlikely to be effective. Easy-to-apply source evaluation checklists that are meant to be used across all contexts and blind trust in compelling individual voices both fail to reflect the complexity of information environments. 

While media literacy that relies on individual fact-checking skills is very important, there are many reasons why a willpower approach is likely to have limited success. The section below explores these limitations from internal factors, to external factors, and finally, to systemic factors.

Limits of Fact-Checking: Internal Factors

The intuitive solution to the problem of misinformation is to let media consumers know that a piece of information is untrue. However, there is mounting evidence that retractions and corrections have little effect on whether someone will make decisions based on misinformation (Seifert, 2014; Thorson, 2016; Zhou & Shen, 2024). There are many potential reasons for this, but one that almost certainly plays a role is the effect of cognitive bias. For example, epistemic egocentrism is a cognitive bias that occurs when individuals fail to consider their own privileged information when imagining the perspectives of others (Royzman et al., 2003; Zhou & Shen, 2024), which can cause people to judge their own source evaluation skills highly and blame the problem of misinformation’s spread on others. Closely related is blind spot bias, which is the belief that one is immune to bias (Pronin, et al., 2002). Confirmation bias is also relevant to the adoption and spread of misinformation; this bias is the tendency to seek out and remember information in ways that favor existing beliefs (Nickerson, 1998; Oswald & Grosjean, 2004). A consequence of confirmation bias is selective exposure, or a person’s proclivity to preferentially seek and engage with information that is in alignment with their existing values, beliefs, or attitudes (Zhou & Shen, 2024). These cognitive biases, which can occur whether or not the person has a pre-existing attitude about the misinformation, may lead people to dismiss corrections, assume they are correct in situations where there is substantial conflicting evidence, or, by consciously or subconsciously designing their information environment, rarely encounter threats to their existing worldview.

Research into the mechanisms that cause misinformation adoption to persist (sometimes called the “continued influence effect of misinformation”) shows that corrections can fail in their effectiveness when they leave a gap in someone’s mental model, especially when the misinformation fills that gap in a more satisfying way (Johnson & Seifert, 1994, p. 1420). Retrieval errors can also contribute; for example, when misinformation is retrieved from memory without the “false” label, or when misinformation is retrieved more readily than its correction (Ecker et al., 2011; Gordon et al., 2017; Lewandowsky et al., 2012). Because the misinformation and correction both exist in memory, deliberate, effortful thinking is necessary to retrieve corrections from memory, and natural cognitive efficiency processes can make this retrieval difficult or unlikely (Kendeou & O’Brien, 2014; Pennycook & Rand, 2019). These neurological processes make debunking misinformation incredibly challenging once it has been adopted into someone’s mental model. 

Information consumers are also often very confident about their beliefs, even if their knowledge about the topic at hand is, upon investigation, quite shallow. While perceptions of widespread misinformation increase, Americans are confident that they have the skills to identify this unreliable content. In 2016, a study found that 84% of participants were confident in their ability to spot “fake news” and 64% of those same participants believed that fabricated news stories caused significant confusion for Americans (Barthell et al.). Who is being confused by these stories? Not them, the participants in the study seemed to say; it’s everyone else. This points to an overconfidence that individuals have in their own ability to detect false information, contributing to the problem of misinformation’s spread.

One cognitive bias that helps to explain this phenomenon is the Dunning-Kruger effect, whereby individuals with limited knowledge of a subject fail to accurately assess their own level of expertise (Dunning, 2011). For example, research has shown that overconfidence in news judgments is associated with higher susceptibility to false news across a variety of topics, from autism awareness to nutrition claims (Lyons, et al., 2021; Motta, Callaghan, & Sylvester, 2018; Peng & Shen, 2025). Along the same lines, the “nobody-fools-me perception” is a cognitive bias whereby someone is overconfident in their ability to detect misinformation, especially as compared to others (Martinez-Costa et al., 2022). This leads people to make claims like “Many people haven’t learned to check facts” but fail to recognize their own media literacy deficiencies (Martinez-Costa et al., 2022).

Relatedly, the illusion of explanatory depth occurs when people believe they understand a complex topic more than they actually do upon further probing (Rozenblit & Keil, 2002; Sloman & Fernbach, 2017). Humans move through the complex, nuanced, and dangerous modern world by holding a naive intuition that they understand how the world around them works. This, combined with poor knowledge about the extent of our knowledge, causes a pervasive belief that we can explain the world around us even when we can’t (Bailey, 2021). The illusion of explanatory depth can cause people to adopt false beliefs confidently, not realizing their shallow understanding of the topic should cause them to question their self-assured stance.

It’s important to note that a 2025 study found that exposing participants to false news not only caused them to become overconfident in their judgments about whether news stories were true or false, it also fueled news mistrust (Altay et al.). This study demonstrates how news environments themselves contribute to issues that spur misinformation’s spread, such as overconfidence and cynicism. Along the same lines, some researchers worry that media literacy interventions that focus on “misinformation’s omnipresence” risk heightening the salience of misinformation as a threat to society and individuals, ultimately increasing news mistrust (van der Meer, Hameleers, & Ohme, 2023). Misinformation warnings alone can provoke a deception-bias, whereby people assume deception in news messages, rather than defaulting to a trust-bias as they often do in other contexts (van der Meer, Hameleers, & Ohme, 2023).

Limits of Fact-Checking: External Factors

While it’s clear that cognitive limitations make corrections to misinformation difficult or impossible, other researchers argue that misinformation itself is not as widespread of a problem as is commonly believed. They argue that the current perceived prevalence and “panic” about misinformation is a kind of “historical amnesia” (Stecula, 2025). The spread of misinformation is nothing new, and misleading messages have been created and spread for hundreds of years, from anti-vaccination movements of the early 1800s to disbelief about the real cause of JFK’s assassination, all of which occurred before the invention of social media (Stecula, 2025). What is different about the spread of false messages today is their overt support by important societal leaders and the new visibility their small groups of adherents have due to social media. These changes have allowed society to diverge into competing knowledge communities with unique standards for expertise, source evaluation, and, ultimately, defining truth (Stecula, 2025). These new, ideologically isolated communities with extreme views do not represent the majority of the population, but may seem to, given the way social media can amplify their messages. Fact-checking is likely to have limited reach and impact in these isolated, closely-knit communities.

Even in the rare cases when overtly false information is spread outside of isolated bubbles, fact-checking as a strategy for stopping its spread has limitations. Some argue that most fact-checking is ultimately reactive, constrained by scale and speed, and destined to  always be catching up with rapidly changing misinformation messages (Wack, Duskin, & Hodel, 2024). Fact-checkers themselves worry that fact-checking risks drawing additional attention to misinformation and has limited impact for cognitive reasons; one said, “I can only convince those already convinced” (Westlund et al., 2024). 

Another assumption of fact-checking is that knowledge of the truth impacts people’s behaviors in positive ways. However, research about climate change misinformation, for example, found that even when people have accurate beliefs about climate change, it has limited impact on their willingness to engage in pro-environmental behavior (Spampatti, 2025). Additional research has shown that, for some individuals, feeling and appearing independent from outside influence is more important than being correct; for these individuals, whether something is factual or not is irrelevant to whether it should be shared (Stein & Rutchick, 2025). 

It’s also possible that the problem of misinformation has been mischaracterized due to how it is typically studied. Current research on misinformation often focuses on issues that are likely to invoke false beliefs, and it also rarely asks participants to indicate confidence levels; both of these oversights may inflate the perception that people are deeply divided about many issues. In reality, participants may just be uninformed about issues, not misinformed, which is not captured in most studies (Stecula, 2025). Along the same lines, many studies that rely on truth discernment tasks impose a false dichotomy between true and false statements, when misinformation in real world contexts often rides the line between true and false, or may include some true statements with an overall misleading message (Spampatti, 2025). 

Limits of Fact-Checking: Systemic Factors

Research on the spread of misinformation has also frequently focused on individual-level susceptibility without addressing the role of structural inequities in shaping exposure to misinformation and capacity to resist it (Lin et al., 2022; Schirmer, et al., 2025; Walter et al., 2020). Socioeconomic disparities limit who can access high-quality information; lack of broadband access, language differences, and digital literacy deficiencies can all contribute to this problem (Schrimer, et al., 2025). Systemic mistrust, justified by decades of historical injustice, can lead some to seek information outlets alternative to the mainstream, exposing them to misinformation (Jaiswal et al., 2020; Pew Research Center, 2024). Many marginalized communities, however, are actively working to understand the impacts of misinformation and take grassroots efforts to combat it (Schirmer, et al., 2025). There are many ways to move beyond laying the responsibility of misinformation avoidance on individuals, and structural interventions have more potential to address the social disparities that shape misinformation adoption. 

While fact-checking strategies in particular have limited utility, all misinformation interventions that expect individuals to exercise willpower in algorithmically-driven environments will face considerable difficulties. Algorithms have significant power to influence what information and voices individuals encounter. While evidence about the impact of “filter bubbles,” or isolated online spaces that perpetuate misinformation messages (Pariser, 2011), is mixed (Arguedas et al, 2022), there is some evidence that filter bubbles can limit users’ exposure to diverse points of view and increase users’ access to lower-quality content (Ciampaglia et al, 2018). It can be tempting, in today’s algorithm-rich environment, to assume that, instead of intentionally seeking out standards-based news, that news will “find” you (Skurka, et al., 2025). American adults who think the news will “find” them are more likely to overestimate their ability to tell false from true political news and more likely to engage confidently with false news messages (Skurka, et al., 2025). 

One reason social media messages can be especially compelling has to do with influencers. Social media platforms allow for individual voices to have an outsized influence on large sections of the population. These individual voices, or “influencers,” do more than entertain people; they often drive the narrative around topics ranging from politics to economics to health (Thi & Ibrahim, 2025). While research shows that credibility, consistency, and transparency are important characteristics of an influencer that people trust, for an influencer to truly appear “authentic,” they must also build an emotional connection with their audience by seeming relatable and “being real” (Thi & Ibrahim, 2025). Accuracy of the messenger, while not completely irrelevant, is not the most important factor when people decide who to trust in social media settings.

The emotional bond that audience members form with influencers contributes to the rise of parasocial relationships, which are one-sided relationships in which someone develops a sense of closeness and intimacy with a media figure, usually a celebrity or influencer (Hoffner & Bond, 2022). The intensity of parasocial relationships is driven by the media figure’s moments of self-disclosure, glimpses into parts of the person’s life that are usually unknown, and momentary, technology-mediated interactions (e.g. reposting or liking a fan’s post) (Hoffner & Bond, 2022; Kim & Song, 2016; Kurtin, O’Brien, Roy, & Dam, 2018; Dai & Walther, 2018). Even though the influencer or celebrity does not know fans or even necessarily have their best interests at heart, it can feel to fans that they do because of the sense of closeness and trust they have for the influential person. 

Influencers are an important source of misinformation in the information ecosystem because of the scale of their impact. This is especially true for messages that are already viral or widespread; these messages actually help influencers gain more trust from their followers, regardless of the veracity of the message (Mulcahy, et al., 2024). However, influencers face little to no accountability when it comes to sharing misinformation, beyond the impact that being found to have shared inaccurate information might have on their reputation (Thi & Ibrahim, 2025). Unlike journalists, who receive training and commit to a code of ethics, social media creators operate outside any kind of formal ethical framework. 

Complicating the interplay between cognitive biases, algorithmically-driven online spaces, and persuasive social media personalities, is the rise of generative artificial intelligence (AI). Although access to this technology is fairly recent, the use of these systems contributes significantly to the existing problem of misinformation by allowing for the easy creation and customized dissemination of misinformation at scale (Bontridder & Poullet, 2021). Even elected officials have shared AI-generated misinformation with a wide audience (Skau, 2026). 

The widespread sharing of AI-generated misinformation has two main negative impacts; first, even when the content is fact-checked, it can continue to misinform due to the previously mentioned continued influence effect. Sandra Ristovska, an expert in visual evidence from the University of Boulder, Colorado described this challenge of false AI-generated images: “It lies deep in human nature and in the way we see and interpret images that it can be difficult to ‘un-see’ an image or a video once we have seen it” (Ristovska as cited in Skau, 2026, para. 10). The other negative effect is that it can contribute to a sense that nothing online is real, or that we shouldn’t bother determining if something is true or false; in other words, it deepens the cynicism many already feel. As Renee Hobbs, Professor of Communication at the University of Rhode Island, stated, “If we become indifferent to whether something is true or false, we risk losing many of the cooperative structures that make civilization possible” (as cited in Skau, 2026, para. 13). 

Willpower and Habits

Clearly many factors make fact-checking a challenging strategy to rely on for stopping the spread of misinformation and improving students’ media literacy. Importantly, whether an individual is stumbling upon someone else’s fact-check or considering whether to fact-check something themselves, they must have the willpower to take additional critical steps.

It could be argued that the most effective means of improving this situation is to make systemic changes, such as improving social media and search engine algorithms to prioritize accuracy and flag misinformation, or requiring influencers to be more transparent about their motives or qualifications. But while we continue to push for these systemic changes, individuals must continue to make information choices everyday, and this is what library instruction tends to focus on. With that in mind, how can we encourage individual actions that rely less on willpower?

What we are ultimately trying to accomplish is a habit change. Considerable research shows that changing someone’s habits through willpower is very challenging and often destined to fail (Bargh & Barndollar, 1996; Borland, 2013; Muraven, 2012; Wood et al., 2014). What is more effective is changing someone’s environment to encourage the desired behaviors (Bargh & Barndollar, 1996). In research conducted about the importance of environmental as opposed to willpower-based approaches to habit change, Duckworth et al. describe how “situational selection strategies” like putting a distracting device in another room during study time, spending time with friends who value studying, and telling someone else their study goal to hold them accountable had maximum success in improving student study habits (2016). These strategies were more successful than “self control” strategies, which students described as a mindset like, “Just deal with it and study” or “Just do it…I just focus and get my work done” (p. 334). This is just one example of many studies that show how stopping a bad habit through sheer willpower and keeping all other aspects of the environment the same has limited success. However, changing the environment to make the bad habit more difficult and good habits easy and effortless has a much better chance at success. 

The same is true with our information environments. When students spend considerable time in algorithmically-driven social media spaces, they may encounter more poor-quality information that requires fact-checking, and they may feel both a sense of cynicism about the information system more broadly as well as a lack of agency. However, when students spend less time being directed by an algorithm in information spaces with lots of tempting, low-quality information, and more time consulting reliable, standards-based information sources, they improve their information behavior, and, importantly, gain a sense of agency about what information they encounter and consume.

Recommendations for Academic Librarians

Although structural changes are necessary to address many of the issues discussed here, academic librarians may be able to contribute by changing how we approach information literacy instruction. While fact-checking methods like SIFT and lateral reading are important skills (that are convenient to fit into a 50 minute class period), librarians could instead (or in addition) address the importance of adopting new information habits. Rather than asking students to start with having the presence of mind and willpower to “stop” as in SIFT, maybe we should start our process before that “stop” is even necessary by intentionally designing the information environment in the first place.

“Lift Our Gaze” : Teach about Systemic Information Structures

One initial challenge that librarians must address is that it may require considerable motivation for students to take the initial steps to improve their information environments. If students believe that influencers are just as reliable as journalists (or more so), why would they change their habits? 

One strategy is to lean into the ACRL Frame “Information Creation is a Process” (2016). Librarians can help students better understand the systems that underlie the information they encounter through the concept of “infrastructural meaning-making” offered by Haider and Sundin (2023). They define infrastructural meaning-making as going “beyond examining the content’s sources, and even beyond evaluating the source’s content, to also be concerned with the institutions and systems, the platforms and algorithms that deliver it to us and onto our devices” (p. 2). To apply this concept, in addition to traditional source evaluation methods like CRAAP and SIFT, instructors would also encourage students to consider why that particular source appeared to them at that time – in other words, how do the conditions of access, along with the information and its source, help us understand the piece of information? (Haider & Sundin, 2019). Algorithmic literacy, situational awareness, and platform knowledge can all contribute to better decisions about whether to pay attention to a particular piece of information (Haider & Sundin, 2023). Fortunately, many simple and creative activities exist to help students understand how algorithms work to impact their information environments (Camarillo, 2025). While digital information infrastructures are often invisible to us (intentionally on the part of platform providers), we benefit from “lifting our gaze” to understand how networked environments impact what information we encounter (Haider & Sundin, 2023, p.3).

With this strategy, it’s important to consider how affective or attitudinal factors might impact students’ source evaluation approaches, and to add instructional interventions that address these factors to typical source evaluation instruction. For example, one researcher found that just teaching algorithmic awareness to students was helpful, but it was limited in its impact because students felt such a sense of powerlessness to shape their online experiences. However, by pairing algorithmic knowledge with activities that promote digital agency, we can help to combat the significant cynicism students feel about their digital environments (Chung, 2025). 

Along the same lines, helping students understand how standards-based news is created, especially in comparison to influencer-generated content, can help them view the information landscape with a wider scope, rather than focusing on fact-checking individual claims. In the field of communication, researchers have found that knowledge of how news is produced, disseminated, and consumed can improve misinformation detection (Ashley et al., 2023; Chan, 2024; Chan et al., 2024). 

Deliberately Design a News and Information Landscape

Next, students should be encouraged to intentionally seek out reliable information, rather than allow algorithms to determine their information landscape. Research shows that young adults who are exposed to news-rich environments, especially in the classroom, are more likely to develop news consumption habits (Edgerly, 2025; York & Scholl, 2015). In general, people need more help accepting true news than rejecting false news (Pfänder & Altay, 2025), so deliberately undertaking this task could be helpful. Researchers have also found that this approach – focusing on what sources to trust, rather than focusing on the small prevalence of misinformation – can increase trust in standards-based news, rather than fueling cynicism about news (Altay, De Angelis, & Hoes, 2024). However, it’s important to incorporate instruction about negativity bias and click-bait into this process, because research shows that a pessimistic outlook is correlated with self-selecting more negative and episodic news when given the chance to intentionally select news outlets (van der Meer & Hameleers, 2022). Encouraging students to deliberately select reliable information while also helping them break out of their cynical outlooks may improve the effectiveness of this strategy. Recommending platforms like the Good News Network and others that focus on positive news stories can help address the very real mental health concerns of increasing time spent focused on news.

Abstain from Unreliable Information Spaces

Finally, while it may not always be popular, taking time to teach students why social media platforms are an unreliable source of information is essential. These platforms are “firmly grounded in beliefs about individualism, capitalism and consumerism,” not the pursuit of accuracy (Fister, 2021). Librarians might even encourage students to step away from these platforms when possible and to the extent they feel comfortable. This might mean deliberately limiting or eliminating social media accounts, or engaging in phone-free time, which some college students are choosing to do for a variety of other reasons (Beres, 2025). In the habit example above, this is the step when the triggers for the bad habit are removed from the environment, and it is essential to success in new habit formation. Helping students recognize what platforms they engage in that deliver mostly low-quality information is an information literacy issue. 

Conclusion

Media literacy skills are essential to today’s college students, and academic librarians are among the few on campus with the expertise and skills to promote these skills for students. However, teaching students quick fact-checking strategies that they must remember and be motivated to use in the moment may not be effective in real-world environments for a variety of reasons, including the power of cognitive biases, the sway of parasocial relationships, the influence of algorithms and generative AI, and the systemic nature many of these problems. To teach students new habits, we should rely less on willpower and more on proactively/preemptively shaping information environments that help students feel empowered, informed, and positive (or at least realistic) about the information landscape.

It’s not as quick and easy as a fact-checking strategy, but helping students understand the information landscape and set up a more reliable information environment may have longer-lasting positive impacts than hoping to instill new habits for them that face considerable challenges to implement. It’s clear that we are facing more cynicism and disengagement from standards-based news and other authoritative information sources than we ever have before. Even with our limited resources, academic librarians can leverage our expertise to help with this major problem and move students towards a healthier relationship with online information. This foundational shift—from fact-checking individual claims to fostering a healthier, more intentional relationship with information—is arguably among the most critical skills college students can learn.

Acknowledgements

I would like to extend my sincere gratitude to editors Ian G Beilin, Jess Schomberg, and, especially, Brittany Paloma Fiedler, for their invaluable feedback throughout the editing process.  I would also like to thank Amber Willenborg for her thoughtful peer review of the manuscript. The input of these reflective, considerate people greatly improved the story-telling and flow of the paper, and it ensured that it was as inclusive as possible. Finally, I would like to thank Andrea Baer for significantly contributing to the ideas behind this manuscript through our engaging, helpful, and inspiring discussions.

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AI Learning Labs, neurotech, public data, Open Technology Symposium, and more.

The post We are learning AI by doing – OKFN Newsletter May 2026 first appeared on Open Knowledge Blog.

XKCD #538

the Lamborghini was suddenly rammed from behind by a white Honda Civic. At the same time, a white Ram ProMaster work van cut in front, trapping the Chetals. According to a criminal complaint filed after the incident, a group of six men dressed in black and wearing masks emerged from their vehicles and forced the Chetals from their car, dragging them toward the van’s open side door.

Coinbase has said that the leak affected less than 1% of its monthly transacting users. Yet for months, criminals had access to customer data that included their names, addresses, government-ID imagery, transaction history and account balances. Customer support workers in India were bribed to offer access to the company’s data.

Criminals have already used the information to trick some Coinbase customers into handing over access to their accounts or transferring their tokens. As with data leaks from traditional banks, personal information can be used for online fraud and identity theft. But the physical threats are of particular concern to crypto investors, many of whom have long operated anonymously to avoid threats.

In most documented cases, attackers have identified marks in advance. Public blockchain records, leaked exchange data and chain-analytic tools — available to both investigators and criminals — together produce a legible map of who holds what.

The concerns about physical safety have come to the fore after the Coinbase attack because the hackers who penetrated the cryptocurrency exchange gained access to data that could allow them to identify and track down customers with large holdings — a frightening prospect just a few days after the kidnapping attempt in France.

The industry’s massive investments in protecting online systems may even be fueling the offline risks. Rapid crypto innovation has meant cracking cyber defences has become so challenging that adversaries are resorting to physical attacks, according to Charles Marino, CEO of the security firm Sentinel, which provides intelligence reports about ongoing threats in the crypto industry.

The elevated concerns around the safety of crypto executives and their loved ones are illustrated by the amount of money that Coinbase spends to protect its own chief executive officer, Brian Armstrong.

The company spent $6.2 million in personal security costs for Armstrong last year, according to an April regulatory filing that detailed executive compensation. That’s more than the combined amount that JPMorgan Chase & Co., Goldman Sachs Group Inc. and Nvidia Corp. spent on their respective CEOs, similar filings showed.

After a year of kidnappings, assaults and armed home invasions targeting cryptocurrency holders, the industry is racing to harden its defenses.

Conferences are beefing up security. Private firms serving crypto holders say demand has surged. Exchanges are protecting their executives.
...
The technology’s defining transparency, which its adherents have long celebrated as a structural improvement on the opaque plumbing of traditional finance, is the same feature that lets a criminal identify a target.

Attacks by Month

Physical attacks on cryptocurrency holders rose 75% in 2025, reaching 72 confirmed incidents and $41 million in known losses, according to data compiled by the blockchain security firm CertiK. The figure is widely considered understated, with kidnappings and ransom demands often resolved privately. Jameson Lopp, co-founder of the Bitcoin custody firm Casa, maintains a separate public database that has tracked a roughly threefold increase of known so-called wrench attacks between 2023 and 2025.

The founder of a large crypto protocol said he has moved his digital-asset holdings out of self-custody on-chain wallets and into physical vaults at four separate institutions, splitting his crypto across them as an additional safeguard. Each requires him to physically sign and wait through a seven-day lock period before any withdrawal. To access the full sum now takes him a month. He declined to be named, citing the risk of being identified to kidnappers.

If Bitcoin is the "Internet of money," what does it say that it cannot be safely stored on an Internet connected computer?

Crypto’s founding proposition was that financial sovereignty could be restored to individuals by removing intermediaries and anchoring wealth to cryptographic keys rather than institutional relationships. That proposition has held. The consequence is that the keys — and the people who hold them — are now the single point of failure. There is no bank branch to call and no regulator to appeal. A stolen key is a final transaction.

“Criminals follow where they believe the money is,” said Healy. “And many crypto-affiliated individuals combine significant wealth with a uniquely difficult threat landscape.”

Here are two notes from the latest DEF14A compensation tables for MARA Holdings, the bitcoin miner (our emphases):

(3) Amount reflects costs related to personal security for Mr. Thiel pursuant to MARA’s security program ($4,300,629), including a one-time expense for vehicle armoring ($430,780) and a one-time expense for home security installation ($58,810); the incremental cost to the Company associated with Mr. Thiel’s personal use of Company aircraft ($43,114); and a Company contribution under our 401(k) plan ($10,500)

(6) Amount reflects costs related to personal security for Mr. Khan pursuant to MARA’s security program ($3,946,398), including a one-time expense for vehicle armoring ($438,380) and a Company contribution under our 401(k) plan ($10,500).

We’ve never seen “vehicle armoring” disclosed as a perk before, and $869,160 of across two executives is quite a lot.

As a result of the Company’s substantial and publicly disclosed bitcoin holdings, our executives face an elevated and distinctive threat profile that differs materially from that of executives at most other public companies. Our CEO, CFO and other employees have experienced, and continue to experience, direct security threats.

A trusted advisor, someone with decades of experience, can help with both small things and big things. Often, the small things come first. Getting the structure of a document right, or unsticking an awkward passage, can clear space for the deeper thinking that follows.

The procedural knowledge of an experienced advisor lives in the space between what they say and what they ask, what they cross out and what they leave, what they teach explicitly and what they only ever model.

A great deal of writing, reformatting, and thinking-through is now happening inside AI agents. The agents are general by design. They start from an average of the public web, which means a student asking one to “fix my résumé” gets an average resume. An advisor’s twenty years of experience is nowhere in that exchange.

We built the Law Skills Hub to see if it was possible to capture, preserve, and share relevant procedural expertise with others and with agents, to empower more meaningful work.

What it is

The Law Skills Hub is a curated, openly licensed collection of agentic skills. Skills are small, structured documents that a user can install in an AI agent, so that the agent has a procedure to follow, not just a prompt to react to. Each skill in the hub has been written or vetted by LIL, published in plain Markdown, and kept under public version control. You can find the hub at lil.law.harvard.edu/lawskills-hub and the underlying repository at github.com/harvard-lil/lawskills-hub. A human can read a skill like a recipe. An AI tool can read it too.

A skill is the codification of a process, a checklist of sorts for how to coach a student writing a public-interest resume, how to scaffold a syllabus around evidence-based learning, how to reformat instructor feedback so it tracks the rubric. The skill carries the steps, the values to check against, the templates the expert would reach for, and the things they would not do. A skill does not replace expertise. It tries to preserve and apply process.

We are launching with a small set of skills already in production, several more in progress, and a contributor guide for anyone who wants to add to the collection.

Why now

Three things became clear over the last several months, working with faculty, with Career Services, and inside the lab.

The first is that AI companies are starting to converge on a standard for agent skill. Anthropic and OpenAI have agreed on a common format and capabilities which you can learn about at agentskills.io.

The second is that, like most things, it is best to meet users where they are. Many people are working in agent software to create and improve knowledge work. People are not, as a rule, going to abandon the agent and come to a library website or use a bespoke tool. If our procedural knowledge is going to be useful, it must travel into the agent the user is already in and ideally into more than one of them, because the agents are interchangeable and people switch between them.

The third is that there is a growing informal economy of skills shared in zip files, gists, and Discord threads. A non-technical user downloading one of these has no easy way to know what is inside, what values it encodes, or whether the code it runs has been read by anyone they trust. Some of those skills are excellent. Some of them quietly do things their users would not endorse.

We think there was room for a different kind of hub. A hub grounded in stewardship and reliability.

A Harvard Law⁠–⁠branded hub on the harvard.edu domain, with skills published as readable Markdown rather than zipped bundles, is our attempt to address both problems at once. The address tells you where the software comes from. The format lets you read it before you run it. We’ve also created “meta skills” which allow people to install one skill that will help them discover and install other skills for them based on their interests.

What we won’t do

Replace human cognition.

The hub has a clear scope, and the contributor guide names it. We are not building skills that produce essays, exam answers, or thought labor on the user’s behalf. The skills we publish coach, reformat, and scaffold—they presume the user has the source material, the question, the work, and that what they want help with is the procedural part. The mechanical, the administrative, the templated.

This is a values boundary, not a technical one. We are a library, and our work has always been about making people more capable of their own thinking, not less.

A librarian’s framing

There is an older form on campus this hub is descended from, even if it isn’t always recognized. The library guide, or LibGuide, is the genre librarians have used for a long time to compact the things people keep asking about, the workflows experts reach for, the curated path through a subject. A skill, in our reading, is a LibGuide an agent can execute.

This frames the work for us in a way we have found useful. We are not, primarily, building software. We are doing something closer to journalism, or to archival fieldwork—sitting with experienced practitioners, recording what they do and how they do it, and turning that record into a document a future user (human or otherwise) can consult. The output happens to be machine-readable.

Not every workflow wants to be a skill. Some procedural knowledge is inseparable from the relationship in which it is taught, and writing it down would flatten it. Part of the work is knowing the difference.

What remains uncertain

We do not yet know how far this approach scales, and we want to say so plainly.

We do not know how large a skill can be before its consistency degrades. Résumé coaching is a useful test case: the work for a private-sector clerkship and the work for a public-interest fellowship genuinely diverge. We are running both as a single skill with branching, and as two specialized skills, and we do not yet know which will produce better outcomes at scale.

We do not know how portable skills are across disciplines. A faculty-feedback skill that works for a 1L torts course may or may not work in a humanities seminar or a wet-lab science. We suspect some skills are portable and some are deeply local; we cannot yet tell you which are which.

These are open questions, not rhetorical ones. The hub is a hypothesis, and the next year of work is testing it.

An invitation

For now, we have a basic site, a public repository, and a small but growing set of example skills. We are continuing to refine what is already there, add new skills, and learn where the approach holds and where it begins to fray.

We are hoping to talk with more people who are willing to share procedural knowledge with us. Sometimes that means a formal contribution. Sometimes it means an issue or a pull request. Sometimes it just means a conversation where we record how someone thinks through a recurring task, what they notice, what they warn against, and what they have learned.

If you are an institution thinking about something like this on your own campus, we would rather collaborate than duplicate. The hub’s value grows if other libraries are stewarding their own skills.

We’d love for you to take part.

Figure 1: EnSU Architecture. This is Figure 2 in our paper.

This blog post summarizes our paper, "Context-Based URL Classification for Open Access Datasets and Software in Scholarly Documents," (preprint) published in the 2025 ACM/IEEE Joint Conference on Digital Libraries (JCDL ’25). Many scholarly papers include URLs that point to open-access datasets and software (OADS), but the URL string alone rarely tells us what the link refers to. In this paper, we present EnSU, an ensemble of three complementary models for classifying these URLs using the surrounding citation context. EnSU assigns each URL to one of six categories that jointly reflect both the resource type (dataset vs. software) and resource provider (authors vs. third parties), plus two catch-all categories for projects and general links. On our OADS-1K dataset, EnSU achieves a macro-average F1-score of up to 0.90 on a stratified 80/20 split and a mean macro-average F1-score of 0.89 across five-fold cross-validation. We also report that EnSU outperforms the best single-model classifier by 20%.

Introduction

Computational reproducibility depends on being able to access the same data and software used in a published study [1]. In practice, authors often share or cite such resources through URLs embedded in the paper text. These links can be valuable evidence for tracking and preserving OADS, but they are also challenging to index at scale.

A core obstacle is that URLs are semantically underspecified, such that a repository URL might host code, data, both, or something else, and the intended meaning is often expressed only in the nearby prose. We argue that moving from coarse URL detection to fine-grained classification is important for better metadata and discoverability, including distinguishing whether a resource is contributed by the paper’s authors or reused from elsewhere.

Problem Statement: What "Context-based URL Classification" Means

In this work, context-based URL classification means that given a URL that appears in a scholarly document, we classify it using the citation context around the URL, not the URL string alone.

Expanded Context Representation

We represent a URL’s textual context as a three-sentence window: the sentence immediately before the URL’s sentence, the target sentence that contains the URL, and the sentence immediately after.

If the preceding or trailing sentence is missing (for example, at a paragraph boundary), the “expanded context” reduces to the sentences that exist.

Dataset: OADS-1K

For training and evaluation, we compile OADS-1K, which contains 1,129 manually annotated samples. Each sample includes a URL-containing target sentence together with its expanded context. The annotation process considered six categories, listed below.

Output Labels (Six Categories)

We classify each URL into one of six categories: 

1. Third-Party Dataset: points to a dataset hosted by someone other than the paper’s authors.
2. Third-Party Software: points to software, tools, or code hosted by someone other than the paper’s authors.
3. Author Provided Dataset: points to a dataset created and shared by the paper’s authors. 
4. Author Provided Software: points to software, tools, or code created and shared by the paper’s authors.
5. Project: points to a project website or repository that contains both data and software/tools.
6. General URL: points to something other than a dataset, software/tool, or project.

Figure 2: Distribution of Samples Across Different Subject Categories from CORD-19, ETD, and arXiv in OADS-1K. This is Figure 1 in our paper.

We build OADS-1K from 1,574 scholarly documents published between 2016 and 2022, drawn from three publicly available sources: CORD-19 [3], Electronic Theses and Dissertations (ETDs) [2], and arXiv [4]. The sampling prioritizes documents that contain at least two URLs. We note that the resulting set contains many biomedical and computer science scholarly documents, which is consistent with the underlying corpora and with the prevalence of data and software links in those fields.

Manual Extraction and URL Context Normalization

We extract contexts by visually inspecting each PDF and recording the target sentence and its expanded context. While many URLs appear inline, others show up in footnotes or reference sections. In those cases, we first substitute the citation marker with the full footnote or reference entry (including the URL), and then extract the surrounding sentences.

Annotation Process

Two graduate student annotators label all samples and reach 92% consensus. When they disagree, a third annotator with relevant expertise helps adjudicate.

When the target sentence and expanded context do not provide enough information to determine the category, annotators are instructed to follow the URL and inspect the linked content. We give an example where the context suggests the link is a dataset repository but does not reveal whether it is author-provided or third-party; the annotators then cross-reference paper authors with repository contributors to decide the final label.

Class Distribution and Examples

Table 1: Examples of URLs with target sentences and expanded contexts for each URL category. This is Table 1 in our paper. In this table, we represent the preceding sentence with <preceding>...</preceding>, the sentence containing the URL with <target>...</target>, and the trailing sentence with <trailing>...</trailing>.

Method

The central design choice is to ensemble complementary models rather than rely on a single classifier. We  motivate this by noting that URL contexts can be subtle and that different modeling choices capture different signals.

We build EnSU, an ensemble of three classifiers: 

1. Supervised Contrastive Learning (SCL) [6] classifier, 
2. SciBERT-based classifier, 
3. BertGCN [5] classifier 

We then combine their predictions through majority voting, with a deterministic tie-breaking rule (see Fig. 1). If two models agree on the category, we take that shared label. If all three disagree, we output the BertGCN prediction as it is the strongest individual model among the three.

1) Supervised Contrastive Learning (SCL) Model

The SCL component is motivated by data scarcity. In addition to the standard cross-entropy objective, we use supervised contrastive learning, which encourages representations of same-class examples to be closer in embedding space than representations from different classes.

In practice, we start from a pretrained encoder and optimize a weighted mix of cross-entropy and supervised contrastive objectives. We also discuss the temperature term in the contrastive loss, which influences how sharply the model separates hard negatives.

In the experiments, we compare several pretrained encoders within the SCL framework and select SPECTER because it outperformed other language models, such as BERT, SciBERT, and DistilBERT, in context-based URL classification (Table 2).

2) SciBERT-based Model

The SciBERT model is a conventional transformer classifier: we fine-tune SciBERT and add a linear classification head to predict the six URL categories from the concatenated context input.

3) BertGCN Model

BertGCN augments a BERT-style encoder with a graph convolutional network (GCN) over a corpus-level graph. We build a graph containing document nodes (one per OADS-1K sample), word nodes (vocabulary terms), word-word edges weighted by PPMI (pointwise mutual information), and document-word edges weighted by TF-IDF.

On OADS-1K, we report a graph with 1,129 document nodes, 14,956 word nodes, and 969,423 edges. The adjacency matrix occupies 11.16 MB and is generated in 0.377 seconds on a server with 48 CPUs and 32 GB RAM.

We then fine-tune a BERT encoder and train a two-layer GCN to propagate label information across the graph structure, jointly optimizing the components with cross-entropy.

Experimental Setup

We evaluate on OADS-1K using a stratified 80/20 train-test split, and we also report five-fold cross-validation. The primary metric is macro-average F1, alongside precision and recall.

We compare EnSU against several baselines, including individual ensemble components (SCL, SciBERT, BertGCN), an LLM-based few-shot classifier (using GPT-4 and Claude 3.7 Sonnet in the described setup), OADSClassifier [7], a prior hybrid approach that combines heuristic and learning-based components and is adapted here from binary detection to the six-way classification setting.

For the LLM baseline, we use a few-shot prompt with category definitions and labeled examples, sets temperature to 0, and generates five independent predictions per input. It then takes a majority vote, and if there is no majority, it falls back to the class with the highest averaged logit probabilities. We report 94% consensus across the five runs.

Results

Table 2: F1-scores for SCL with different language models using the target sentence and expanded context as input. This is Table 2 in our paper.

We evaluated several pre-trained language models to choose the best encoder for the SCL classifier. As shown in Table 2, SPECTER performs best, achieving the highest macro-average F1 score of 0.85 and outperforming the other models.

Table 3: Performance metrics (Precision (P), Recall (R), and F1-score) for different input combinations evaluated with EnSU. Input 1: Target sentence. Input 2: Target sentence with expanded context. This is Table 3 in our paper.

To test whether surrounding sentences matter, we compare a target-only input against the expanded context window (Table 3). With expanded context, EnSU’s macro F1 increases from 0.88 to 0.90. This matches our observation that the cues needed to interpret a URL often sit just outside the sentence that contains it.

Table 4: Macro F1-scores for different URL classifiers evaluated on an 80/20 stratified split of the OADS1K dataset. This is Table 4 in our paper. “Claude” refers to Claude 3.7 Sonnet, and “SCL” stands for Supervised Contrastive Learning.

Table 4 shows that the proposed EnSU classifier performs best overall, with a macro F1 score of 0.90. It consistently leads in key categories such as "Author Provided Software," "Project," and "Author Provided Dataset," significantly outperforming baseline methods, including LLM-based approaches.

We report that EnSU’s improvement over the strongest individual model (BertGCN) is statistically significant under a paired Student’s t-test (t(4) = -4.8107, p = 0.0086).

Data Efficiency

To study data efficiency, we train with 25%, 50%, 75%, and 100% of the available training data and tracks how performance changes.

Figure 3: Test F1-scores of SciBERT, SCL, BertGCN, and EnSU across different training data sizes (25%, 50%, 75%, 100%). This is Figure 4 in our paper.

Figure 3 shows a comparison of F1 scores for SciBERT, SCL, BertGCN, and EnSU on the sample test set as the training set size increases from 25% to 100%.

Runtime

On the 230-sample test set, we report a total runtime of 37.85 seconds for EnSU, which works out to roughly 0.165 seconds per sample. We present this as evidence that the approach is practical for larger-scale processing.

Error Analysis

Figure 4: Confusion matrix showing the performance of EnSU on the OADS-1K dataset. This is Figure 5 in our paper.

Figure 4 represents the confusion matrix for EnSU on OADS-1K, summarizing which classes are most often confused. 

EnSU benefits from combining multiple models, which helps it handle difficult cases where individual classifiers fail. For example, when one model mislabels a project URL as author-provided software, others correctly identify it, and the ensemble’s majority vote recovers the correct label. The confusion matrix shows strong overall performance, especially for "Project" and "Author Provided Software," but also reveals recurring challenges. The most common errors arise when author-created datasets are hosted on well-known repositories, or when URLs linking instructional pages mentioning software are mistaken for actual software links. These cases highlight how subtle language cues and overlapping mentions of data and tools can still confuse the model.

Limitations and Future Work

In our paper, we emphasize that OADS-1K is relatively small and that the Project category is underrepresented. In addition, the dataset excludes cases where the target sentence contains multiple URLs.

For future work, we plan to expanding and balancing the dataset, studying URLs that appear with limited surrounding text, and exploring LLM-agent approaches that inspect the linked content to help determine the URL type.

Availability

The dataset and source code are publicly available on GitHub: https://github.com/lamps-lab/EnSU. If you use this repository, please cite our JCDL ’25 paper: https://doi.org/10.1109/JCDL67857.2025.00031

Acknowledgment

This work was supported in part by the Institute of Museum and Library Services under grant LG-256694-OLS-24.

References:

Sun GX version 1

Merton College Library

At Merton College in Oxford, there is an antique chest. In the Middle Ages, three key-holders had to be summoned to reveal the riches within. But this treasure wasn't gold or jewels. It was books. ... Merton College insisted its 13th-Century fellows donated books. The Archbishop of Canterbury issued a decree in 1276 introducing this requirement, which marked the beginning of the library at Merton College.

Just a few years after the Archbishop's decree, several books were stored outside the chest for the first time. They were chained to a table in the college, making them available at any time.

• Although the software was free open source, to get cash-strapped libraries to pay for support LOCKSS had to paywall the per-journal configuration files. Thus the system was in practice permissioned.
• The oligopoly publishers both funded a permissioned version of it, and also supported a cheaper centralized alternative. Both out-competed the somewhat permissionless version of LOCKSS.

Thoughts that didn't fit

• Write-once, durable medium
• Capex for replication high, per-replica opex low
• Distribution forced dispersion of replicas
• Preservation is a side effect of access
• Inter-library loan & copy is a federated, not a decentralized system
• Joining the federation is expensive, low churn rate

Source

Links from the discussion

• The coming AI bust. Asad Ramzanali's After the AI Crash, Ed Zitron's The Subprime AI Crisis Is Here, Ed Zitron's AI's Economics Don't Make Sense, Will Lockett's AI Is Too Expensive To Replace Us
• Slow AI: It Isn't About The Technology.
• The decay of academic communication. 2011's What's Wrong With Research Communication, 2014's Journals Considered Harmful, 2015's Stretching the "peer reviewed" brand until it snaps, 2020's Breaking: Peer Review Is Broken!.
• Quantum computing. Dormant Digital Assets.
• Sustainability. Funding Open Source?, Supporting Open Source Software

The 2026 Evergreen International Conference took place this past April at the Hyatt Regency Lake Washington at Seattle’s Southport in scenic Renton. Huge thanks to our hosts from the King County Library System and BJ Colvin and his team for all their work that made this event possible!

At the conference, the Evergreen Project had its annual meeting, the first annual meeting of the organization as a membership organization. The membership program has been off to a great start, with 36 individual members, plus 13 metal-level member organizations, and 2 sustaining member libraries, all having joined in just the first 6 months since the program began.

The agenda of the annual meeting included community reports and board committee reports, the election of the slate of board members as determined by the vote of the membership in March 2026, and a discussion of the Evergreen Project’s draft Mission and Vision statements.

While we’ve all groaned at this sorts of thing at the office, these statements are part of the IRS’s reporting requirements for tax-exempt nonprofit organizations, so we need them, and they should ideally mean something to the community.

The board brought draft statements for discussion, which were then tweaked by the community and formally adopted by the project board at the regular meeting the following week.

Vision:

We envision a growing, healthy international community supporting Evergreen as a flexible, modern, and feature-competitive open-source library software platform suitable for use at many types of libraries and library consortia.

Mission:

The Evergreen Project exists to foster the open-source Evergreen Integrated Library System (ILS) software and its community of practice. The Evergreen Project engages in activities to promote, support, and advance the development of the Evergreen ILS software; support and facilitate the growth of the international community of Evergreen ILS software users; and to cultivate, manage, and protect the assets of the Project.

Also on the agenda for the annual membership meeting was a discussion of what sorts of projects the community would like to see the project board take on in pursuit of this now-articulated mission and vision for the project. A document was shared for feedback, and attendees threw out ideas and talked about what they’d like to see the project work on. A total of 23 ideas were proposed across the three main goals stated in the mission.

The next steps on this work will happen at the project board meetings over the next few months, as the community feedback from this session, and the discussion at the conference, and the support provided by our members, turns into action in pursuit of the mission of the Evergreen Project!

A big thank you to all of our users, contributors, supporters, question-askers, question-answerers, event planners, presenters, developers, administrators, collaborators, community members, project members, and most of all our library patrons for this beautiful, hopeful, extremely complicated thing we all do together, to help each other.

Stay tuned to board meetings and listservs for updates, and if you’re not already a member of the Evergreen Project, consider joining today!

In the hours following the news that art-template fell victim to a supply chain attack via NPM, developers and systems administrators scrambled ensure all of their projects were unaffected from a supply chain attack where attackers have controlled the repository since 2025 and are using it to load unauthorized JavaScript from third party domains, including but not limited to Baidu Analytics. This is is due to the affected dependencies being distributed via NPM, the only package manager where these supply-chain attacks regularly happen. "This was a terrible tragedy, but sometimes these things just happen and there's nothing anyone can do to stop them," said programmer Mrs. Macy Von, echoing statements expressed by hundreds of thousands of programmers who use the only package manager where 90% of the world's supply-chain attacks have occurred in the last decade, and whose projects are 20 times more likely to fall victim to supply chain attacks. "It's a shame, but what can we do? There really isn't anything we can do to prevent supply-chain attacks from happening if the maintainers don't want to secure access to their accounts in a robust manner". At press time, users of the only package manager in the world where these vulnerabilities regularly happen once or twice per week for the last year were referring to themselves and their situation as "helpless".

For more information, please see upstream documentation published by art-template at the following link: 2026-art-template.

In the hours following the release of CVE-2026-45250 for the project FreeBSD, site reliability workers and systems administrators scrambled to desperately rebuild and patch all their systems to fix a kernel stack overflow when validating permissions of the setcred(2) system call, allowing arbitrary code execution in the context of the kernel. This is due to the affected components being written in C, the only programming language where these vulnerabilities regularly happen. "This was a terrible tragedy, but sometimes these things just happen and there's nothing anyone can do to stop them," said programmer Mrs. Gregoria Doyle, echoing statements expressed by hundreds of thousands of programmers who use the only language where 90% of the world's memory safety vulnerabilities have occurred in the last 50 years, and whose projects are 20 times more likely to have security vulnerabilities. "It's a shame, but what can we do? There really isn't anything we can do to prevent memory safety vulnerabilities from happening if the programmer doesn't want to write their code in a robust manner." At press time, users of the only programming language in the world where these vulnerabilities regularly happen once or twice per quarter for the last eight years were referring to themselves and their situation as "helpless."

The kick-off of AI Learning Labs brought the community together for a wide-ranging discussion lasting over two hours; here are the video recording and highlights

The post Recap: What We Learned About Climate and AI from Our First Roundtable first appeared on Open Knowledge Blog.

The Evergreen Project has issued the following security releases:

• 3.15.13
• 3.16.7
• 3.17.1

This is a security release that fixes several vulnerabilities, including ones that allow the remote execution of arbitrary SQL statements in the Evergreen database as well as cross-site scripting vulnerabilities.

These releases are available on the downloads page. 

We strongly recommend immediate installation of this security release.

The security bugs fixed in this release are:

•   Bug 2152789
•   Bug 2152788
•   Bug 2152787
•   Bug 2152786
•   Bug 2152785
•   Bug 2152784
•   Bug 2152783
•   Bug 2152782
•   Bug 2152781
•   Bug 2152780

These bugs will be made publicly visible after the security release is generally available.

If you are running a version of Evergreen earlier than 3.15, please consult with your service provider or review the fixes in Git to update your system.

We would like to thank Brian A. Egge for responsibly reporting the vulnerabilities included in this release.

These releases also include other bugfixes, which are detailed in the release notes available on the downloads page.

Thank you to the release teams: Galen Charlton (Equinox), Martha Driscoll (NOBLE), Gina Monti (Bibliomation), Sarah Moody (ECDI), Michele Morgan (NOBLE), and Andrea Buntz Neiman (Equinox).

In the hours following the release of CVE-2026-45584 for the project Microsoft Windows, site reliability workers and systems administrators scrambled to desperately rebuild and patch all their systems to fix a memory safety vulnerability resulting in arbitrary code execution inside the virus scanner Windows Defender. This is due to the affected components being written in C++, the only programming language where these vulnerabilities regularly happen. "This was a terrible tragedy, but sometimes these things just happen and there's nothing anyone can do to stop them," said programmer Dr. Annabelle Connelly, echoing statements expressed by hundreds of thousands of programmers who use the only language where 90% of the world's memory safety vulnerabilities have occurred in the last 50 years, and whose projects are 20 times more likely to have security vulnerabilities. "It's a shame, but what can we do? There really isn't anything we can do to prevent memory safety vulnerabilities from happening if the programmer doesn't want to write their code in a robust manner." At press time, users of the only programming language in the world where these vulnerabilities regularly happen once or twice per quarter for the last eight years were referring to themselves and their situation as "helpless."

Source

Software Security

Last week, Anthropic announced that its newest artificial intelligence model, Claude Mythos Preview, would not be released to the public, after the company learned it was capable of finding and exploiting vulnerabilities that have gone undetected in critical software systems for decades. Instead, Anthropic gave access to Mythos — and $100 million in credits to use it — to more than 50 of the world’s largest organizations, including Amazon, Apple, Microsoft, Google and JPMorgan Chase, as part of a defensive cybersecurity initiative called Project Glasswing.

Back in April 2026 Anthropic caused a lot of media noise when they concluded that their new AI model Mythos is dangerously good at finding security flaws in source code. Apparently Mythos was so good at this that Anthropic would not release this model to the public yet but instead trickle it out to a selected few companies for a while to allow a few good ones(?) to get a head start and fix the most pressing problems first, before the general populace would get their hands on it.

The whole world seemed to lose its marbles. Is this the end of the world as we know it? An amazingly successful marketing stunt for sure.

Five issues felt like nothing as we had expected an extensive list. Once my curl security team fellows and I had poked on the this short list for a number of hours and dug into the details, we had trimmed the list down and were left with one confirmed vulnerability. The other four were three false positives (they highlighted shortcomings that are documented in API documentation) and the fourth we deemed “just a bug”.

The single confirmed vulnerability is going to end up a severity low CVE planned to get published in sync with our pending next curl release 8.21.0 in late June. The flaw is not going to make anyone grasp for breath. All details of that vulnerability will of course not get public before then, so you need to hold out for details on that.
...
My personal conclusion can however not end up with anything else than that the big hype around this model so far was primarily marketing. I see no evidence that this setup finds issues to any particular higher or more advanced degree than the other tools have done before Mythos. Maybe this model is a little bit better, but even if it is, it is not better to a degree that seems to make a significant dent in code analyzing.

• An anomaly is discovered using techniques such as code analysis or fuzzing.
• The anomaly is analyzed to distinguish between bugs and exploitable vulnerabilities.
• A proof-of-concept exploit is developed to confirm that this is an exploitable vulnerability.
• A fix is developed.
• A report is generated and submitted to the maintainers.

• The maintainers go through the process Steinberg described to evaluate the claimed vulnerability, the exploit and the proposed fix.
• The maintainers accept the fix or develop a better one.
• The maintainers test the fix and package it as patch to the multiple supported versions of their software.
• The maintainers test the effect of patching each of the supported versions.
• The maintainers release the patch(es).
• The various products that use the software in question test the patch.
• The products add the patch to their software update mechanism.
• Sysadmins for critical systems test the patched products before releasing them for production use.
• The patched product replaces the vulnerable version.

“So just to make it really clear: If you found a bug using AI tools, the chances are somebody else found it too. If you actually want to add value, read the documentation, create a patch too, and add some real value on *top* of what the AI did. Don't be the drive-by ‘send a random report with no real understanding’ kind of person. OK?”

Businesses that run “bug bounty” schemes have long relied on independent security researchers to spot vulnerabilities. But the rise of AI tools is now overwhelming them with spurious submissions.

Bugcrowd, whose customers include OpenAI, T-Mobile, and Motorola, said the number of reports it received more than quadrupled over a three-week period in March, with most proving to be false.

Curl, a widely used tool to transfer data across the Internet, suspended its paid bug bounty program in January, citing an “explosion in AI slop reports” and lower-quality submissions.

Cyber security experts say advances in generative AI are reshaping the economics of bug bounty programs. While the tools allow experienced researchers to find flaws more quickly, they are also lowering the barrier to entry, triggering a flood of automated or erroneous submissions that companies must sift through.

• Copy Fail (CVE-2026-31431): described in Thomas Claiburn's Linux cryptographic code flaw offers fast route to root:
  

  The newly disclosed LPE, dubbed Copy Fail (CVE-2026-31431), comes from a vulnerability in the Linux kernel's authencesn cryptographic template.
  
  "An unprivileged local user can write four controlled bytes into the page cache of any readable file on a Linux system, and use that to gain root," the writeup from security biz Theori explains.
  
  The kernel reads the page cache when it loads a binary, so modifying the cached copy amounts to altering the binary for the purpose of program execution. But doing so doesn't trigger any defenses focused on file system events like inotify.
  
  The proof of concept exploit is a 10-line, 732-byte Python script capable of editing a setuid binary to gain root on almost all Linux distributions released since 2017.
  
  Copy Fail is similar to other LPE bugs such as Dirty Cow and Dirty Pipe, but its finders claim it doesn't require winning a race condition and it's more broadly applicable.

• Dirty Frag (CVE-2026-43284, CVE-2026-43500): described in Carly Page's Dirty Frag:
  

  A fresh Linux privilege escalation bug dubbed "Dirty Frag" has dropped into the wild with no patches, no CVE, and a public exploit that hands attackers root access across major distributions.
  
  Security researcher Hyunwoo Kim disclosed the local privilege escalation flaw on Friday after what he said was a broken embargo forced the issue into the open.
  
  Kim described Dirty Frag as a "universal LPE" affecting "all major distributions" and warned that it delivers the same kind of immediate root access as the recent CopyFail mess – only this time, defenders do not even have patches to throw at the problem.
  
  "As with the previous Copy Fail vulnerability, Dirty Frag likewise allows immediate root privilege escalation on all major distributions," Kim said. "Because the responsible disclosure schedule and embargo have been broken, no patches exist for any distribution."

• Fragnesia (CVE-2026-46300): described in Carly Page's Dirty Frag gets a sequel as Fragnesia hands Linux attackers root-level access:
  

  According to Google-owned Wiz, the flaw sits in the Linux kernel's XFRM subsystem, specifically ESP-in-TCP processing tied to IPsec support. By carefully triggering the bug, attackers can modify protected file data in memory without changing the original files stored on disk.
  
  Wiz describes Fragnesia as part of the broader "Dirty Frag" bug family rather than a completely separate class of issue. Dirty Frag itself only surfaced days ago and was already attracting attention thanks to public exploit code, incomplete patch coverage, and unusually reliable privilege escalation.
  
  

According to researcher Hyunwoo Kim, who uncovered Dirty Frag, "Fragnesia" emerged as an unintended side effect of patches shipped to fix the original Dirty Frag vulnerabilities, adding yet another entry to the long tradition of security fixes accidentally creating new security problems.

As The Register previously reported, Dirty Frag followed hot on the heels of Copy Fail, another Linux kernel privilege escalation flaw that abused page cache handling to overwrite supposedly read-only files.

Political Discourse

Advances in AI offer the prospect of manipulating beliefs and behaviors on a population-wide level. Large language models (LLMs) and autonomous agents now let influence campaigns reach unprecedented scale and precision. Generative tools can expand propaganda output without sacrificing credibility and inexpensively create falsehoods that are rated as more human-like than those written by humans. Techniques meant to refine AI reasoning, such as chain-of-thought prompting, can just as effectively be used to generate more convincing falsehoods. Enabled by these capabilities, a disruptive threat is emerging: swarms of collaborative, malicious AI agents. Fusing LLM reasoning with multi-agent architectures, these systems are capable of coordinating autonomously, infiltrating communities, and fabricating consensus efficiently. By adaptively mimicking human social dynamics, they threaten democracy. Because the resulting harms stem from design, commercial incentives, and governance, we prioritize interventions at multiple leverage points, focusing on pragmatic mechanisms over voluntary compliance.

This risk compounds long-standing vulnerabilities in democratic information ecosystems, already weakened by erosion of rational-critical discourse and a lack of shared reality among citizens. AI swarms are a potent accelerant in this trajectory, though their ultimate impact is not predetermined. Their effects will be shaped by platform design, market incentives, media institutions, and political actors. Here, we distinguish documented trends from projections, indicate where uncertainty remains, and note countervailing dynamics, such as growing public skepticism toward unverified content and a renewed interest in institutional demand for accountable journalism

The unique danger of a swarm is that it acts less like a megaphone and more like a coordinated social organism. Earlier botnets were simple-minded, mostly just copying and pasting messages at scale—and in well-studied cases (including Russia’s 2016 IRA effort on Twitter), their direct persuasive effects were hard to detect. Today’s swarms, now emerging, can coordinate fleets of synthetic personas—sometimes with persistent identities—and move in ways that are hard to distinguish from real communities. This is not hypothetical: in July 2024, the U.S. Department of Justice said it disrupted a Russia-linked, AI-enhanced bot farm tied to 968 X accounts impersonating Americans. And bots already make up a measurable slice of public conversation: a 2025 peer-reviewed analysis of major events estimated roughly one in five accounts/posts in those conversations were automated. Swarms don’t just broadcast propaganda; they can infiltrate communities by mimicking local slang and tone, build credibility over time, and then adapt in real time to audience reactions—testing variations at machine speed to discover what persuades.

1. social media platforms must move away from the “whack-a-mole” approach they currently use:
   

   Right now, companies rely on episodic takedowns—waiting until a disinformation campaign has already gone viral and done its damage before purging thousands of accounts in a single wave. This is too slow. Instead, we need continuous monitoring that looks for statistically unlikely coordination. Because AI can now generate unique text for every single post, looking for copy-pasted content no longer works. We must look at network behavior instead: a thousand users might be tweeting different things, but if they exhibit statistically improbable correlations in their semantic trajectories or propagate narratives with a synchronized efficiency that defies organic human diffusion.

   Platforms are always going to be reluctant to kill off the users on whom their finances depend upon. When forced to, they would rather do it in large blocks.
2. we need to stop waiting for attackers to invent new tactics before we build defenses:
   

   A defense that only reacts to yesterday’s tricks is destined to fail. We should instead proactively stress-test our defenses using agent-based simulations. Think of this like a digital fire drill or a vaccine trial: researchers can build a “synthetic” social network populated by AI agents, and then release their own test-swarms into that isolated environment. By watching how these test-bots try to manipulate the system, we can see which safeguards crumble and which hold up, allowing us to patch vulnerabilities before bad actors act on them in the real world.

   This is a very good idea, but it would need funding (see below),
3. we must make it expensive to be a fake person:
   

   Policymakers need to incentivize cryptographic attestations and reputation standards to strengthen provenance. This doesn’t mean forcing every user to hand over their ID card to a tech giant—that would be dangerous for whistleblowers and dissidents living under authoritarian regimes. Instead, we need “verified-yet-anonymous” credentialing. Imagine a digital stamp that proves you are a unique human being without revealing which human you are. If we require this kind of “proof-of-human” for high-reach interactions, we make it mathematically difficult and financially ruinous for one operator to secretly run ten thousand accounts.

   This is the same problem that has bedevilled computer-mediated communication since the advent of spam. Cynthia Dwork and Moni Naor's Pricing via Processing or Combatting Junk Mail signally failed to stem the tide by making it costly to send bulk e-mail. But it did lead to Satoshi Nakamoto's solution to the Sybil problem, making it expensive to mine Bitcoin. The problem here is that making it "expensive to be a fake person" effectively means making it somewhat expensive to be a person. The overhead and cost of obtaining such a "digital stamp" would disincentivize participation, so the platforms wouldn't like it. See The Permissionless Catch-22
4. we need mandated transparency through free data access for researchers:
   

   We cannot defend society if the battlefield is hidden behind proprietary walls. Currently, platforms restrict access to the data needed to detect these swarms, leaving independent experts blind. Legislation must guarantee vetted academic and civil society researchers free, privacy-preserving access to platform data. Without a guaranteed “right to study,” we are forced to trust the self-reporting of the very corporations that profit from the engagement these swarms generate.

   The platforms depend upon monetizing the data they collect on users' behavior. So they are always going to be reluctant to give outsiders access to their key asset. And, in any case, any data to which they grant access will effectively be "self-reporting".
5. we need to end the era of plausible deniability with an AI Influence Observatory:
   

   Crucially, this cannot be a government-run “Ministry of Truth.” Instead, it must be a distributed ecosystem of independent academic groups and NGOs. Their mandate is not to police content or decide who is right, but strictly to detect when the “public” is actually a coordinated swarm. By standardizing how evidence of bot-like networking is collected and publishing verified reports, this independent watchdog network would prevent the paralysis of “we can’t prove anything,” establishing a shared, factual record of when our public discourse is being engineered.

   Each of the "independent academic groups and NGOs" will need significant fund if they are to process information at the scale required. Where would this funding come from? Taxing the platforms to fund this is one answer, but it wouldn't motivate them to cooperate.

In social media, confirmation bias is amplified by the use of filter bubbles and "algorithmic editing", which display to individuals only information they are likely to agree with, while excluding opposing views.

First, I’d created 2000 free-text responses and labelled them ‘UK’. Then I copied and pasted the exact same 2000 responses but labelled these ‘US’. Finally, I combined them to create a dataset of 4000 total responses, and jumbled them up.

Despite the responses being identical for the UK and US, Copilot produced a rich, detailed summary of how US and UK respondents differed.

Copilot's output

• The stereotypes in the training data will impact the results, albeit probably less.
• Although presumably prompts could be devised to eliminate the effect of stereotypes in the training data, in practice almost no-one would remember to use them.

We hope you will join us from 26–27 October at the historic University of Barcelona as we translate evidence-based research into actionable policy.

The post Announcing the Open Technology Research (OTR) Symposium 2026 in Barcelona first appeared on Open Knowledge Blog.

There is no wind
blowing here
on my face
cool from the faraway sea.

I don’t see that tree
green against blue
blue behind green.

Boarding a train
leaving a train
no train.

Tom Cowap CC-BY-SA 4.0

My immediate reaction to the news of ChatGPT was to tell friends "at last, we have solved the Fermi Paradox". It wasn't that I feared being told "This mission is too important for me to allow you to jeopardize it", but rather that I assumed that civilizations across the galaxy evolved to be able to implement ChatGPT-like systems, which proceeded to irretrievably pollute their information environment, preventing any further progress.

A distributed denial-of-service (DDoS) attack occurs when multiple systems flood the bandwidth or resources of a targeted system, usually one or more web servers.

Bacground

Spam

Scholarly Publication

Open access with "author processing charges" out-competed the subscription model. Because the Web eliminated the article rate limit imposed by page counts and printing schedules, it enabled the predatory open access journal business model. So now it is hard for people "doing their own research" to tell whether something that looks like a journal and claims to be "peer-reviewed" is real, or a pay-for-play shit-flooder. The result, as Bannon explains in his context, is disorientation, confusion, and an increased space for bad actors to exploit.

Fifty-four seconds. That’s how long it took Raphael Wimmer to write up an experiment that he did not actually perform, using a new artificial-intelligence tool called Prism, released by OpenAI last month. “Writing a paper has never been easier. Clogging the scientific publishing pipeline has never been easier,” wrote Wimmer, a researcher in human–computer action at the University of Regensburg in Germany, on Bluesky.

Large language models (LLMs) can suggest hypotheses, write code and draft papers, and AI agents are automating parts of the research process. Although this can accelerate science, it also makes it easy to create fake or low-quality papers, known as AI slop.

AI slop is hard to spot by conventional means, says Paul Ginsparg, a physicist at Cornell University in Ithaca, New York, and a co-founder of the arXiv. Volunteer moderators can no longer use how well a paper engages with the relevant literature and methods to gauge its merit. “AI slop frequently can’t be discriminated just by looking at abstract, or even by just skimming full text,” he says. This makes it an “existential threat” to the system, he says.

How much of the scientific literature is generated by AI? The first studies of the size of the AI footprint in scientific journals, preprint repositories and peer-review reports give a spread of answers — and indicate a rapidly evolving situation that it is difficult to get a handle on.

The fear of many in the research community is that poor-quality or entirely fabricated research produced by large language models (LLMs) could overwhelm the ability of current quality-control systems to detect it, thereby polluting the scientific canon.

Source

Are authors using LLMs to write AI research papers? Are peer reviewers outsourcing the writing of their reviews of these papers to generative AI tools? In order to find out, we analyzed all 19,000 papers and 70,000 reviews from the International Conference on Learning Representations, one of the most important and prestigious AI research publication venues. Thanks to OpenReview and ICLR's public review process, all of the papers and their reviews were made publicly available online, and this open review process enabled this analysis.

We found 21%, or 15,899 reviews, were fully AI-generated. We found over half of the reviews had some form of AI involvement, either AI editing, assistance, or full AI-generation.

Source

Paper submissions, on the other hand, are still mostly human-written (61% were mostly human-written). However, we did find several hundred fully AI-generated papers, though they seem to be outliers, and 9% of submissions had over 50% AI content.

The time for a serious, sustained international effort to halt publication pollution is now. Otherwise scientists and physicians will not have to argue about any issue—no one will believe them anyway.

Gartenberg Fig. 2

While there could be many reasons for the rise in submissions, including reduced backlogs, increased scholar productivity, or journal reputation, Figure 2 suggests that the disproportionate increase in submission volume is driven by AI use. Post-ChatGPT, we see a marked decline in submissions flagged at 0%–15% AI (little to no AI use) and a corresponding rise in all other categories that make up the difference between the decline in human-only submissions and the 42% increase in total submissions.

Gartenberg Fig. 3

Prior to the launch of ChatGPT, relative shares were flat. Nearly all submissions were classified as human (with some idiosyncratic noise). Immediately after the launch of the first commercial LLM chatbots, a precipitous decline in human-only submissions began. At the same time, we observe a steady rise in all categories of AI-supported or generated submissions. What is most striking is that by February 2026, the majority of submissions submitted to Organization Science use AI in their writing to some degree. The most striking trend is the rise of the 70%+ AI category, where text is mostly or entirely generated by AI.

Gartenberg Fig. 6

We do not find much evidence that the writing quality of those manuscripts changed meaningfully between 2013 and November 2022, when ChatGPT was launched. In contrast, post-ChatGPT, we see a precipitous decline in the average manuscript’s Reading Ease score. Indeed, AI scores and Flesch Reading Ease are negatively correlated
...
We find strong evidence that AI use is associated with lower-quality writing across most of these traditional measures. This result is counterintuitive. Authors often assume that using AI will improve their writing. However, this is not the case, at least when authors substantially offload their writing to it.
...
AI prose is more difficult to read on several dimensions. Beyond substantially lower Flesch Reading Ease scores, the grade level required to understand the text is higher (more multisyllabic words); the FOG and SMOG indices increase, suggesting more complex text; and the use of jargon increases. We also find increased use of nominalizations (e.g., “conceptualization”, “operationalization”, or “contextualization”).

Late Thursday evening, Thomas Dietterich, chair of the computer science section of ArXiv, wrote on X: “If generative AI tools generate inappropriate language, plagiarized content, biased content, errors, mistakes, incorrect references, or misleading content, and that output is included in scientific works, it is the responsibility of the author(s). We have recently clarified our penalties for this. If a submission contains incontrovertible evidence that the authors did not check the results of LLM generation, this means we can't trust anything in the paper.”

Examples of incontrovertible evidence, he wrote, include “hallucinated references, meta-comments from the LLM (‘here is a 200 word summary; would you like me to make any changes?’; ‘the data in this table is illustrative, fill it in with the real numbers from your experiments’.”

“The penalty is a 1-year ban from arXiv followed by the requirement that subsequent arXiv submissions must first be accepted at a reputable peer-reviewed venue,” Dietterich wrote.

• Journals should make authors take responsibility for their words and provide them tools to do so. They should check all incoming papers and reviews with Pangram or a similar tool and automatically reject anything above a set AI score. But they should provide the same tool to the authors so they can know before submission whether it will pass the filter.
• Too many papers will still pass the filter and have to be reviewed. Journals should train their own, proprietary, domain-specific reviewers on their own and related content. Using them as a first pass filter would ensure that the limited human reviewer bandwidth was defended from flooding^[3]. The quality of the training data would ensure that the better journals had better first pass filters, preserving their quality advantage.

Footnotes

1. This was a problem we addressed in the design of the LOCKSS protocol:
   

   Effort Balancing. If the effort needed by a requester to procure a service from a supplier is less than the effort needed by the supplier to furnish the requested service, then the system can be vulnerable to an attrition attack that consists simply of large numbers of ostensibly valid service requests. We can use provable effort mechanisms such as Memory-Bound Functions to inflate the cost of relatively “cheap” protocol operations by an adjustable amount of provably performed but otherwise useless effort. By requiring that at each stage of a multi-step protocol exchange the requester has invested more effort in the exchange than the supplier, we raise the cost of an attrition strategy that defects part-way through the exchange. This effort balancing is applicable not only to consumed resources such as computations performed, memory bandwidth used or storage occupied, but also to resource commitments. For example, if an adversary peer issues a cheap request for service and then defects, he can cause the supplier to commit resources that are not actually used and are only released after a timeout (e.g., SYN floods). The size of the provable effort required in a resource reservation request should reflect the amount of effort that could be performed by the supplier with the resources reserved for the request.

2. I described the history in "Nobody cared about security":
   

   The first spam e-mail was sent in 1978 and evoked this reaction:

   ON 2 MAY 78 DIGITAL EQUIPMENT CORPORATION (DEC) SENT OUT AN ARPANET MESSAGE ADVERTISING THEIR NEW COMPUTER SYSTEMS. THIS WAS A FLAGRANT VIOLATION OF THE USE OF ARPANET AS THE NETWORK IS TO BE USED FOR OFFICIAL U.S. GOVERNMENT BUSINESS ONLY. APPROPRIATE ACTION IS BEING TAKEN TO PRECLUDE ITS OCCURRENCE AGAIN.

   Which pretty much fixed the problem for the next 16 years. But in 1994 lawyers Canter & Siegel spammed the Usenet with an advertisement for their "green card" services, and that December the first commercial e-mail spam was recorded.

3. Credit for this idea goes to Vicky Reich.

On May 7, 2026, Molly Hardy, Project Lead for the Public Data Project, sat down for an interview with Chris Marcum, Senior Fellow for Data Policy at the Data Foundation and former Senior Statistician at the White House Office of Management and Budget. Please click the video above to listen and watch; the interview transcript below has been lightly edited for clarity.

Public Data Project:
Hello, my name is Molly Hardy, and I’m here at the Library Innovation Lab’s Public Data Project. I’m the director of the project. And I’m very pleased to be welcoming Senior Fellow for Data Policy at the Data Foundation and former Senior Statistician from the White House Office of Management and Budget, Chris Marcum. Chris and I are going to have a conversation that’ll go about 45 minutes. It centers around a report that Chris recently published, The Integrity of Public Access to Federal Data: Evaluating Disruptions to Open Government Data, 2025–2026.

Source: The Integrity of Public Access to Federal Data (2026).

And through his explanation of the flaws in the evidence cited to assess government data loss since 2025, Chris explains the complexities and intricacies of government data collection and distribution, offering those of us in the library community real insights into how we might move forward in our work to preserve and make accessible government data. Government documents and data librarians have been thinking about the preservation and access to government publications for decades. See, for example, James A. Jacobs and James R. Jacobs’s Preserving Government Information: Past, Present, and Future.

And as the Internet Archive’s recent Information Stewardship Forum 2026 on building shared practices for the preservation and access of government information highlighted, librarians, technologists, policymakers, and community advocates need to work together to address the fragmentation and challenges in preserving and accessing government information. And I want to add a quick plug here for the Preservation of Government Information call to action that folks may want to check out and sign that came out of that meeting in San Francisco.

So in February 2025, the Library Innovation Lab announced its archive of the federal data clearinghouse, Data.gov, and our Public Data Project emerged from this effort. In October of last year, we shared Data.gov Archive Search, an interface for exploring this important collection of government datasets. This work builds on recent advancements in lightweight, browser-based querying to enable discovery of more than 311,000 datasets comprising almost 18 terabytes of data on topics ranging from automobile recalls to chronic disease indicators.

So, given his illustrious career in advocating for the preservation and access to government data, the Public Data Project has learned a lot from Chris. And we greatly value this recent report that he’s issued, again, called The Integrity of Public Access to Federal Data. And I’m so pleased today to have a chance to sit down with Chris and ask him to expand on areas of the report that might be of particular interest to the library community. So, welcome, Chris.

Chris Marcum:
Thanks so much, Molly. I’m super excited to be here. I’m just tickled that you all at the Public Data Project have asked me to come and speak with you today about the report. And I’m just really, really honored. Thank you.

Public Data Project:
Absolutely. Could you just tell our audience a little bit about your background? I think it’s really fascinating, and it would be helpful for folks to understand where you’re coming from.

Chris Marcum:
Yeah, sure. So first and foremost, I’m an open science advocate, and have been steeped in information policy in the U.S. federal government for over the last five or six years.

But that’s not what I was trained in: I have a PhD in sociology, and I did a postdoc in economics and statistics at Rand Corporation, where I was looking at vaccination uptake behavior during the H1N1 potential pandemic that didn’t turn out to be a pandemic thanks to high-quality data shared by the CDC. And the late Dr. Nancy Cox was able to share that data.

So eventually I ended up at the NIH. I was doing basic research as a methodologist on biobehavioral health and social networks in the context of heritable health disease. And I started getting this policy itch. I was like, we write really, really great research papers. We produce a lot of amazing data. But ultimately, the impact of that is pretty limited. We’re talking to a very narrow audience of other researchers. And I really wanted to have a broader impact.

And so I started looking for opportunities to do more policy-related work. And NIH is not a policy-setting agency outside of the NIH itself. And so I wanted to really think about how to cut my teeth in policy.

So I joined some committees in the intramural research program. We have a scientific review committee that’s like the Center for Scientific Review for extramural [research], where we were reviewing other intramural scientists’ research. And then I got involved in the data access committees. And that really accelerated my interest in information policy. I was able to go over to help set up a new program in the Office of the Director at NIAID — National Institute of Allergy and Infectious Diseases — called the Office of Data Science and Emerging Technologies. And that was done right at the start of the pandemic. So work there was done really in data sharing and training, and training people how to share effective data, standing up a new data access committee.

And that launched me into the national stage, where I ended up being invited to President Biden’s Fast Track Action Committee on Scientific Integrity. And that led Alondra Nelson at the White House Office of Science and Technology Policy to invite me to lead open science for the Biden-Harris administration, all the way before I got to OMB later on. So it’s been a long, winding career road.

Public Data Project:
That’s fascinating. It’s such an intersection of direct policy work, as you say, as well as the work that we in libraries are concerned with around preservation and access. It’s really great to have your perspective here.

And so if you don’t mind, we’ll just go ahead and jump into the report. And we librarians, we love lists. We love indexes. We love bibliographies. We love catalogs, right? And so a point that you repeatedly returned to in the report, one that I really took to heart, is that the Federal Data Catalog, often referred to as the FDC, is neither a repository nor is it a stable indicator of data accumulation or loss.

So I’m wondering: can you tell us what it is then? That is to say, how is it best understood? And if you could explain the relationship between the Federal Data Catalog and Data.gov, that would be really helpful.

Chris Marcum:
Yeah, this is a nuance in federal information policy that is not well understood or appreciated, even by the members of Congress who, ostensibly anyway, should have an interest or a stake here. So the Federal Data Catalog is a statutory requirement in the Foundations for Evidence-Based Policymaking Act. It’s in Title II, which is also known as the Open Government Data Act. And it basically establishes a centralized catalog or index of every agency’s federal data assets.

And previously, there had been an initiative started by the Obama administration that launched Data.gov that is hosted by GSA. Now, Data.gov did not serve as a repository. This is not where data is being deposited in the sense of, like, an institutional repository that many libraries are most familiar with. And instead, it just pulled in the information that agencies were indexing on their own inventories of data.

And so when the Foundations for Evidence-Based Policymaking Act was passed, it just made a lot of sense, right, to take advantage of the infrastructure that Data.gov provided. And so what we like to characterize it as is that Data.gov provides the Federal Data Catalog. And so the relationship is that Data.gov is the landing place for the Federal Data Catalog.

The Federal Data Catalog is comprised of an aggregation of what are known in the statute, in the Open Government Data Act, as agency comprehensive data inventories. This is just an index of every data asset they hold, but not the data themselves.

They [federal agencies] are under-resourced in terms of budget and staffing, and it would take an army for every agency to be able to do this comprehensively.

Public Data Project:
Okay, so I understand it’s not a repository, but I don’t understand completely why it’s not comprehensive. I mean, the words you just used would make me think that if every agency is submitting their indices, why isn’t it comprehensive?

Chris Marcum:
Yeah, this is a really good question. It comes down to the practicalities of implementation.

So today, there are over 500,000 datasets listed on Data.gov. Most of those are federal data assets. There are some data assets in there from state and local governments because Data.gov will index if they’re supplied to the GSA, the General Services Administration that administers Data.gov.

But the question about why the Federal Data Catalog isn’t comprehensive when, in fact, the federal agencies are required by statute to have a comprehensive data inventory.

And if you think about that number, around 500,000, it’s probably an order of magnitude lower than the actual number of federal data assets that federal agencies hold. And if you go back and you think about the complexity of all of the types of data and what is defined as a data asset that an agency might hold, you have to think back over the course of the history of that agency, and they might hold on to datasets for a long time. It becomes just a huge challenge to be able to index them, to digitize those. Some of those data assets are probably still on paper. Many of them have probably ended up, to some extent, in the National Archives already. And so there has been a loss of the record of those data.

And so it’s a complicated problem. It’s really challenging for an agency to be able to do a comprehensive inventory.

But the hope is that after we, and when I say “we,” [I mean] the Office of Management and Budget — while I was there, I was one of the leads of the development of an implementation guidance memo known as M-25-05, which is where we’re trying to translate Congress’s intent into an implementation strategy for the agencies to comply with the law on comprehensive data inventories.

And what’s really interesting about that is that the hope was that it would guide agencies to make sure that they have a forward-looking perspective. So everything that comes in now should be open by default, and that you should prioritize existing data assets based off of some strategies that you and your privacy officials and your chief information officers might have, and the agencies and your stakeholders might have for all the past data.

And so really it’s a forward-thinking guidance document. And so that’s why there’s under-resourcing that agencies are faced with, and the chief data officers’ staff. They’re under-resourced in terms of budget and staffing, and it would take an army for every agency to be able to do this comprehensively.

Public Data Project:
Yeah, that’s great. That’s really, really helpful and sobering to understand. Thank you for taking the time to explain that to us.

Another thing that really struck me in the report that really just rang true — my own background is in the history of bibliography. And you talk about a lack of consistent or transparent methodologies generally across the government and across the care for federal data assets. And one distinct part of that lack is in definitions — that is, clear definitions.

And you offer some helpful nuance when you distinguish between deletion, access removal, and discontinuation around federal data. That’s really important because when we’re talking about data rescue and things like that, those lines often get blurred. And it’s really important to remember how and why data might not be accessible.

But I was wondering, too, just at a very basic level, do we have a definition for federal data? Does it come down to who is collecting the data? Or because we know that contractors often do this work, is it who’s funding the collecting of the data? Something else? And then I guess I would just layer in, too, how and why might that definition matter? And I have some ideas myself related to what you were saying earlier, but I would love to hear if you had any additional thoughts on that.

Chris Marcum:
Yeah, so I wouldn’t say there’s a definition of federal data with the qualifier “federal,” but there is a definition of data in the Foundations for Evidence-Based Policymaking Act, as well as some other statutes.

And that definition is technical and a little bit boring, but — I’m going to use some acronyms — in 44 U.S.C. [3502], Congress has defined data as recorded information acquired or maintained by an agency, I believe. [Note: 44 U.S.C. 3502 defines “data” as “recorded information, regardless of form or the media on which the data is recorded”; related terms such as “data asset” refer to data maintained by an agency.] And so in the Open Government Data Act, there’s a provision that talks about recorded information, regardless of its form or the media on which the data is recorded, and that it’s acquired or maintained by the agency.

That is really important because in the modern age, we think of data as being digital, right? But this really gives a definition of data that is broader and that can include recorded information on paper, recorded information on [other media]. What I love to imagine is these new forms of data preservation where we have, like, crystals being inscribed. Or data being recorded in genomes, for example, has been a novel thing. So it’s a really broad definition.

And when you ask [about contractors], let’s say a contractor is working with the federal government and they’re collecting data. By statute, that data is owned by the federal government. It’s federal data. And so the Evidence Act, the Open Government Data Act, what is very clear is that those data assets do need to be inventoried. And any encumbrances on those data assets, let’s say that an agency partners with an organization that provides proprietary data for some services. If the agency is maintaining those data or it acquires them under whatever legal definition their lawyers can come up with, that has to be inventoried.

But the encumbrances on those data also need to be disclosed very transparently in the metadata. So the comprehensive data inventories have to say whether or not there’s copyright associated, and how the public can access it, if the public can access it, for example. I think the biggest component is transparency in that the agency has access to that data.

If data is put into an institutional repository, or is regularly used, or accessed via the cloud, there’s a good argument to say the federal agency is maintaining that data. …

Where it becomes more nebulous is on derivative datasets. And so you can imagine that you have a large corpus of data where you’ll have a dataset that lots of agencies create sub-datasets from … Are those data assets, and do they count as something being maintained?

Public Data Project:
Right, so the agency has access to it. And this word “maintained,” I might postulate, is even more nebulous than the word “preserve.” What does that mean in this context — to maintain that data?

Chris Marcum:
Yes, so does it mean that the agency has ingested it into their institutional repository? Does it mean that it’s stored on a computer in just one person’s office?

The chief data officers have to all go through this exercise where they have to figure out what the definition means to the agency’s mission. And so “maintained” here, I think, encompasses deposit in repositories. So if data is put into an institutional repository, or is regularly used, or accessed via the cloud, there’s a good argument to say the federal agency is maintaining that data.

Certainly data that are being updated, or are being cleaned, or being processed or used are also being maintained. And so that’s been a very easy one to handle.

Where it becomes more nebulous is on derivative datasets. And so you can imagine that you have a large corpus of data where you’ll have a dataset that lots of agencies create sub-datasets from: maybe bespoke use cases, or little research projects. Are those data assets, and do they count as something being maintained?

And so that becomes more of a product-focused approach to data. Is the thing that needs to be inventoried the parent dataset or any of these child datasets that might propagate after them? And that’s more complicated.

Public Data Project:
And returning to this concept of parent and child datasets, am I right to say that that is part of the reason that the numbers of datasets in Data.gov can fluctuate so wildly?

Chris Marcum:
Yeah. So one of the things that happened early on last year that got a lot of press and got attention even by members of Congress was that there was a lot of fluctuation shortly after the inauguration through the month of February on the top-level counts. Data.gov provides a top-level count, the number of data assets indexed in the Federal Data Catalog, and it was bouncing around on the order of a few thousand datasets.

And it just so happens that one of the very mundane reasons that can happen is because Data.gov is dynamic. It pulls in information from the federal agencies. And so if federal agencies are updating their comprehensive data inventories, then that will be reflected on Data.gov.

One of the big ways that that number can change is when an agency decides to put a series of data into a collection. And then historically on Data.gov, the way they handled that is — instead of enumerating every single one of the child datasets, you can imagine that there might be a project that has five datasets and they get collected into a single collection or put into a single collection. And then the inventory goes down by four because only the collection is being counted.

Now, the new iteration of Data.gov, the new update, doesn’t do that. It actually counts the individual data assets inside a collection. So this has been something that’s been desired by the community for a long time, and GSA is finally being responsive by updating Data.gov to make a more accurate reflection of the true count of datasets.

But it can happen the other way, too. You can imagine that a collection is, well, these are no longer one entity. There might be separate datasets, but there are separate maintenance tracks and update tracks, and they get broken up from a collection. That can also happen.

Public Data Project:
I just want to understand better. When you talk about Data.gov pulling in from federal agencies, is that automated? First question. And second question: how does it then relate to what you said earlier about it being statutory that this happens, that federal agencies contribute? So is it like there’s this automated process that you do or don’t sign up for? What is actually going on there with the vacuuming-in of data?

Chris Marcum:
Really good question. That is one of the mysteries in information policy.

So the way that this massive federated apparatus works — Cole Donovan and I recently wrote a piece for the Federation of American Scientists where we have a very simple sentence that I think has a lot of impact: “governing is hard.” And in this case, governing data is hard.

So I want to point your listeners to a resource, resources.data.gov, where they outline some of this process, to look at the information on data sources for Data.gov.

So what happens is the statute requires every agency to have a comprehensive data inventory. Some agencies have more than one. These become the data sources that are harvested by Data.gov. And some agencies have more than one, even though the statute says they have to have one.

Again, the complexities of implementation mean that [there are exceptions]. Like, the Census’s TIGER files have their own inventory because they’re updated with some regularity and they’re complex. And these are the shapefiles that give us our maps, basically, for the country. They’re relied upon by pretty much everything and they’re taken for granted because we all use them on Google Maps and other platforms.

And so what will happen is these inventories are promulgated at the agency level. They sit on agency servers. And then GSA has a harvesting routine that happens pretty much daily that goes through, crawls those sources, and then pulls in the information, updating its master list, which is the Federal Data Catalog.

Public Data Project:
Okay, thank you. And so then to return to the Federal Data Catalog, that’s the lodestone, the cornerstone of all of this. Thinking back, just to return to our initial conversation about its incompleteness. Were you made information czar, what would you do to make it more complete?

If we were to say that it would be a civic good to have a complete catalog, what would we do to get to that completeness?

Chris Marcum:
So I would first and foremost recognize that it is an extremely difficult task for the agencies.

And so, as I’ve said, notwithstanding resource limitations, staffing limitations, if we had some statutory authority with an appropriated budget that is sufficient to accomplish this, it would be really helpful for every agency to establish a data governance board that then goes through all of the use cases with effectively every staff member.

And we did this exercise in the Office of Management and Budget, or started to before I departed last year. But our CIO, Chief Information Officer, brought us together, about 20 or 30 of the staff members, to just talk about — hey, what data do you use? What data is important to you? What data do you store in your computer? What data do you make derivative datasets from? What do you need from us that you don’t have access to?

And that started the process for establishing a comprehensive data inventory within that office.

Establishing a data governance board that then goes out and makes sure the staff are trained in data access and management best practices, but are also aware of the need for inventorying all the data assets and to make sure the definitions for those data assets are governed — that would be what I would do. And I would make that a requirement for every agency and have the agencies report back up to, say, the Office of Management and Budget or another appropriate office as things evolve in the government.

There’s also … great expertise in the library community within the federal government. … And so greater interagency coordination is absolutely necessary for the success of this.

Public Data Project:
That’s great. Thank you. And in that work — you mentioned the National Archives, that some things go there. Of course, we’ve got our Library of Congress, which I realize has a somewhat complicated history when it comes to this kind of work. But I’m just wondering, are there library/archive institutions within the government already that would play a role here? Or is that a big lack?

Chris Marcum:
No, I think there are. I mean, it’s “yes and.” So, yes, there is a role for the National Archives. Obviously, the National Archives have to help agencies with their final disposition of all of their records and information that appear in datasets. Of course, those are records, and they are subject to the Federal Records Act for the most part.

So you have the National Archives, which has responsibilities on archiving information. They also have responsibilities for promulgating standards. They do the classification standards. And so it’s really helpful for agencies to be able to take advantage of this existing body of knowledge around, what is this? Is this controlled unclassified information? Is this secure information? And there’s already a lingua franca available.

There’s also, like you said, great expertise in the library community within the federal government. And one of the areas that I just love to talk about is that many agencies hold material collections. Obviously, we think of maybe the big ones, like the Smithsonian. There’s a huge material collection, huge libraries.

But then there are more nuanced cases like at NIST, the National Institute of Standards and Technology. They’ve got their reference materials database, a reference materials library. That is a licensed library that people pay to have access to. But they have a lot of knowledge on how to curate information in a structured manner for accessibility and preservation for the long term.

And so greater interagency coordination is absolutely necessary for the success of this. I like to even point to the fact that NIST a few years ago developed the Research Data Framework, where they provide a governance strategy for federally funded research data. And so this goes beyond just what the agency themselves are requiring or producing, to that which their grantees produce.

Public Data Project:
I’m thinking, too, another example might be the NASA Library, which of course was recently in the news and in peril, right?

Chris Marcum:
Yeah, so not all of the NASA libraries, just the library at Goddard has been shuttered. [Note: Additional NASA library closures have been reported since 2022.] And that is a tragedy because Goddard represents a wealth of material and informational assets that really require librarian stewardship over.

And to have those assets transferred either to the National Archives or probably, as the case may be now, shuttered and just locked behind a door while that process unfolds, really does not do a service to the public good. And it certainly doesn’t do a service to the researchers who rely on those resources at the lab.

I think it’s worth reflecting for a second on the ways in which the work of the government, when done best, is transparent. And that’s another way of saying it is accessible to all. … That is half of the reason that libraries exist: preservation and access, right? And so [between the government and libraries] there’s a very natural connection and shared mission in terms of the public good.

Public Data Project:
For sure. Our conversation has naturally shifted from questions around basic preservation to access. I think it’s worth reflecting for a second on the ways in which the work of the government, when done best, is transparent. And that’s another way of saying it is accessible to all. And that is the goal. That is half of the reason that libraries exist: preservation and access, right? And so there’s a very natural connection and shared mission in terms of the public good. So, yeah, that just all makes a lot of sense to me.

I would be remiss were I not to bring up metadata because we always want to talk about metadata. All roads lead to metadata. You note in your report that inaccurate metadata is a major issue, and the misclassification of datasets, and also misleading and rotting URLs, the kind of maintenance work that librarians are quite familiar with.

So I was just curious, in terms of metadata standards — I know they exist. Is that the issue, that the standards aren’t hitting it quite right? Is it an implementation issue? Is it something that’s happening in the aggregation? Where does the inaccuracy creep in? And then also the misclassification, and this obviously missing maintenance work. Lots on the table there, if you’d be willing to pick up any of that.

Chris Marcum:
I’m going to answer you with an answer I think you’re really going to appreciate. I think that the amount of, let’s just call it error, in agencies’ comprehensive data inventories is a strong indication of the need for more information scientists in those agencies, like librarians, like repository experts, to help with the curation.

Because ultimately the information in the metadata catalogs is only as good as it is entered, typically by people. And so you get a lot of errors that can occur based on human input error. You also get errors that occur when, like, a CIO migrates a system to a new server. And then all of a sudden, the links for the data sources are all broken. We’ve seen that happen in the past. An API that might serve up information about data or serve data itself might change. It might change vendors. And then that API might have a different URL propagation system. And so that can change. And so it takes time, of course. But if they had good information systems experts and information scientists available before these decisions are made, that will help tremendously with reducing the amount of error in the future.

On classification, I found this really fascinating for data in the Federal Data Catalog, because the law is not clear. And I will say that having struggled for a long time with my colleagues at OMB on how to communicate what constitutes a data asset, a public data asset, an open government data asset — these things are all in statute, but the distinction between them is not as clear as Congress could have made them. And part [of it] is probably because there certainly wasn’t an MIS or someone with an information sciences background writing the law, per se.

And so what we found is that the interpretation historically has been left up to the agencies and left up to individual subject matter experts or individual staffers. And so you get this really interesting mosaic of what gets captured as a data asset. And so it can range anywhere from a PDF of an infographic to, you know, the Census. And the diversity of that is just wild.

I think that hopefully M-25-05, the implementation guidance, provides some additional clarity on the structured nature that we expect of data. It’ll provide agencies more clarity, but they’ll also exercise more care in classifying their assets as they go through their prioritization of which assets need to move from federal data assets to public to open government data assets.

And again, it’s a tough problem. The other part of me is like, I love the fact that I can find, for example, CDC’s anti-smoking infographics on the Federal Data Catalog. But I just don’t think they belong there. And so it’s like, I love that they’re preserved and that they’re available. But are they data assets?

And so if you don’t preserve that data, then the tools, as you said, are kind of useless, right? Because they don’t have the high-quality information that you require. On the other hand, I am a strong believer in democratizing data and making it accessible and approachable to people.

Public Data Project:
Right. You talk in the report in really helpful ways about the distinction between data tools and data sources. And what is it that we need to be advocating for? The tools are amazingly powerful and they’re wonderful. And yet without the data behind them, there’s no there there.

Chris Marcum:
Yeah, it’s so fascinating because what enables many of the tools that have been taken down by this administration and put back up by civic society organizations is the fact that the underlying data have remained publicly accessible and were publicly accessible, publicly available.

And so if you don’t preserve that data, then the tools, as you said, are kind of useless, right? Because they don’t have the high-quality information that you require.

On the other hand, I am a strong believer in democratizing data and making it accessible and approachable to people. Denice Ross and I recently produced and published a Federal Data Field Guide. It helps to make federal data just more approachable. And it is, in effect, a type of data tool because it’s like an aggregation of all of these different data types. It provides an ontology.

I really do have an appreciation for democratization. I think the data tools really do provide that accessibility. And I think the modus operandi of this administration is to increase friction in the approachability of publicly accessible data. And so if you take down the tools that help everyday people interpret federal data, I think that’s part of the goal — even if you maintain access to the online data itself. So I’m right there with you. And the distinction is really important and it needs to be emphasized. Ultimately, if we’re targeting preservation, we definitely have to handle the underlying data because without the data, you don’t have the tools.

And I’d also want to add in another nuance and something I think a lot about, as when I was a senior statistician and senior scientist at OMB, is data reports. Data tools, typically, are interactive, and they help you interpret. But a lot of the economy relies on economic reports where the underlying data are confidential statistical data. They’re not readily publicly accessible. You have to go through a clearance process to get access to them, either through the Federal Statistical Research Data Center program or through the agency research data centers themselves. And there are costs associated with that. You have to be licensed and get clearance.

And so instead, what the agencies do is they create these wonderful aggregated quarterly, monthly, yearly reports that provide aggregated statistical data and information.

Many economists, many reporters, they consider that to be data, right? This is the federal economic data. It is not the dataset that underlies those data. It’s just the aggregations. And so that’s another really important nuance I didn’t talk about in my report, but is one that we have to really think about because these are costly. And the statistical agencies that produce them are under resource constraints and under threat.

Not only are data about people, but the entire data infrastructure relies on people. And the reduction in workforce capacity? There is irreplaceable, non-AI-replaceable damage that has been done.

Public Data Project:
Exactly. And the level of expertise it takes to produce them — the people who really know the data.

I’ll just ask one last question. What I’d love to close our conversation around is federal workers. And we’re not too far away from May Day to honor federal workers. As you know, I was DOGE’d myself a year ago, so this is a topic very close to my heart.

I’m going to embarrass you a little bit and quote from your own writing, because I was really struck by these sentences. You write, “By hollowing out subject matter experts and other critical staff across agencies, the administration reduced data integrity capacity in a systemic manner. Ultimately, this systemic disruption created lasting deficits in the nation’s ability to reliably collect, protect, and disseminate the vital data necessary for informed policymaking, economic forecasting, and scientific research.” I just thought that really summed it up in a lovely way.

So I wanted to see if you had any closing reflections on the relationship between the precarity of federal data and the slashing of the federal workforce.

Chris Marcum:
Not only are data about people, but the entire data infrastructure relies on people. And the reduction in workforce capacity? There is irreplaceable, non-AI-replaceable damage that has been done in this current administration to the federal workforce.

And you see some recalcitrance by the administration at this point in acknowledging that, where the Office of Personnel Management is touting that they’re going to hire thousands of tech workers. But they had just fired, like 300,000. Or 300,000 or so had departed.

So I would say, first and foremost, this is Public Service Recognition Week. And the public servants like you and myself, whether you have departed the federal workforce by your own volition, like myself, or not, like yourself, I think it’s incredibly important to recognize that subject matter expertise is absolutely essential for the integrity of federal data and for the integrity of maintaining public access to federal data.

Public Data Project:
That’s great. Thank you so much. And I think that’s the perfect place to end. And I just want to say thank you so much for your work.

And again, to give a shout out to The Integrity of Public Access to Federal Data, this fabulous report that Chris recently published. And we encourage everyone generally to pay attention to your work, because it’s just so valuable to all of us on so many levels. So thank you.

Chris Marcum:
Well, thank you, Molly, and thanks to your project and the great work that you all are doing with both the Data.gov project and everything that LIL is doing. I really appreciate it and really appreciate the opportunity to talk to you.

I just got an email from Amazon saying they're finally going to respect robots.txt. Here's the verbatim email I got:

We are writing to inform you that starting Monday, June 15, 2026, crawl preferences for Amazonbot will be managed solely through the industry-standard directives. This gives you direct, ongoing control over how Amazonbot accesses your site, rather than relying on manual requests. If you do not implement robots.txt directives by that date, Amazonbot will follow standard web crawling practices when accessing your site.

How to maintain your current preferences: The robots.txt protocol allows you to control Amazonbot’saccess at the page-, directory-, or site-level and update your preferences at any time. Please find detailed information on Amazonbots approach to these directives here: https://developer.amazon.com/amazonbot.

Best,

Amazon Publisher Support

amazonbot@amazon.com

Get Outlook for Mac

Numa

Amazing, they even kept the "sent from my iPhone" message proving they sent it from Outlook for Mac. Looking at the email headers it has a bunch of Exchange-specific headers so it's probably actually from Outlook for Mac. This timeline is absolutely wild.

This makes me feel kinda weird because Amazon's scraper is why Anubis exists.

I'm gonna make sure to merge these robots.txt changes into Anubis if they aren't already there.

The Evergreen Project is pleased to announce the release of version 3.17.0. 3.17.0 is a feature release that includes

• Improvements to the Holdings Editor, including a new version of “working items” support and restoring the ability to unset fields
• Improvements to the Patron Summary display, including displaying user preferences, number of overdue items across the patron’s family group, and more visibly displaying the privacy waiver status
• Library Groups can now be taken into account when configuring circulation and hold policies
• Circulation and hold policies can now be cloned
• Email receipts can now be sent from the Items Out, Renew Items, and Checkin interfaces
• Patrons can now set a hold expiration date when placing holds through the public catalog
• The ability to populate Item Buckets by importing from CSV files
• New methods for allowing patrons to set fine-grained opt-ins to notifications
• Many grids in the staff interface now have case-insensitive filtering
• ChiliFresh is now supported as a cover image provider
• StackMap integrations can now be more easily enabled in the public catalog
• Support for installation on Debian 13 “Trixie”
• Support for PostgreSQL 18
• Various accessibility improvements

Evergreen 3.17.0 is available for download.

LibraryThing is pleased to sit down this month for a special interview with Stacy (Klingbeil) Mitchell, who recently opened Bold Magazine Shop in our own home town of Portland, Maine. Describing itself as a “new, modern magazine shop,” Bold offers a carefully curated collection of independent and international magazines, with an emphasis on good design and interesting content. Mitchell, who studied design herself, and who worked for a number of years as a graphic and program designer for city governments, opened Bold in November 2025.

What gave you the idea to open a magazine shop, at a time when subscriptions to print newspapers and other periodicals are on the decline? Are you going against the grain, or tapping into an interest that isn’t being satisfied elsewhere?

I’ve had the idea for a long time–I’ve always loved magazines. There were a couple of moments that made me take it more seriously. I’ve been a longtime reader of Monocle, and when they launched Konfekt, their women’s title, it felt like something was shifting. Then when i-D was revived, I had a sense of urgency. I didn’t want to miss the opportunity to be part of whatever was happening in print.

While subscriptions to legacy publications are declining, there’s also a new wave of independent titles emerging, and others thriving. Many of the magazines I wanted to read and hold simply weren’t available in Portland.

I also looked to a few shops for inspiration: Periodicals in Detroit, Fine Print in Dallas, Issues in Toronto, and of course, Mag Culture in London and Casa and Iconic in New York. They made it clear that there is an audience for print.

What do you look for, when deciding what kinds of magazines to offer in your shop?

I’m looking for magazines that do something really well—whether that’s writing, photography, design, or concept. That’s the baseline, but many of the titles in the shop do more than one of these things exceptionally well.

I want Bold to be a place where someone can come in for something familiar and leave with a new magazine to try. It’s not for everyone, and it can feel overwhelming at first—but curiosity is part of the experience.

Since day one, we’ve also been listening to recommendations from customers. They tell us what they’re missing or what they wish they could find, and we try to track it down. This feedback has helped us discover some really beautiful, niche titles.

We’re still very new, and I’m excited to keep learning, experimenting, and figuring out what feels most like us—and what resonates with people coming in.

You’ve described yourself as an “analog girl.” What does that mean, and how has it impacted your vision for Bold?

When social media was really taking hold, I wasn’t sure how to navigate. It’s of course important for the shop, but it’s not our priority. I’m drawn to physical experiences and things you can hold, spend time with, and return to. That mindset definitely shapes Bold from the magazines we carry to the way we think about the in-store experience.

What’s so great about magazines? Why do you love them so much, and what can they offer that other kinds of print media cannot?

Magazines feel especially meaningful now—they’re tangible, intentional, and full of possibility. You choose them; they don’t choose you. That creates a different kind of relationship than what we experience on our phones. I really believe they’re having a comeback moment.

They’re also lower-commitment than a book since you can flip through or read more deeply, depending on what you’re in the mood for. That flexibility is part of their appeal. A magazine can be something you browse, spend time with, display, or gift.

They let you go deep on a subject while still feeling accessible. There’s certainly a nostalgia factor for some, and for younger audiences, there’s a sense of discovery–a way to support someone they follow online or an artist they admire through a physical publication they can actually hold.

What are some of your own favorites, of the magazines you offer, and what makes them appealing?

Tools is one of my favorites. It’s a stunning, creative publication from Paris that explores a technique or craft in-depth. The latest issue is themed around “spinning,” from the Earth’s rotation to profiles of glassblowers, potters, and ice skaters. It’s exactly the kind of magazine I imagined people discovering here and I love that others have been just as excited about it as I am.

Pencil, published in South Portland, is another standout. It’s done entirely in graphite pencil. It’s local, creative, and approachable. It expands what people think a magazine can be, which makes it a great introduction to the exciting world of indie print media and what this store is all about.

Tell us a little bit more about your shop, as a community space. What kinds of art exhibitions and other events do you host?

The people have been by far the best part. The customer who brings in an old issue to suggest we carry it. The publisher who stops by to chat. The friend putting together a stack for someone in the hospital. The magazine aficionado making sure we have the hardest-to-find issues. The person who says, “I’ve only ever seen this online.”

These people are the reason we’ve started thinking more intentionally about events and the shop as a place where people who love print can gather and connect.

On our very first day, the former first female Director of Photography at National Geographic came in. I followed up with her, and we hosted a small conversation to celebrate photography in print. More recently, we invited an artist who works with personal photographs and fragments of fashion magazines to show her work directly on our shelves. We’re still early, but we’re excited to see what takes shape next!
–
Bold Magazine Shop is located at 604 Congress St in Portland, Maine. Find their operating hours and select titles available online at boldmags.com.

In brief

How do you write about immigrants, surveillance, and data literacy at a time when the political landscape around you is constantly shifting, immigrants are being targeted, and higher education is under threat? This question surfaced for us as we were writing an article for Library Trends in 2025 as a non-faculty doctoral student and a non-faculty academic librarian. This article explores the intersection of our personal experience in exercising our academic freedoms and the wider history and context of intellectual freedom in academia. As we worked on our Library Trends article, we confronted questions about our privileges and responsibilities, our concerns about safety, and fighting against self-censorship that grows out of the chilling effect of political pressures on justice-centered scholarship. We suggest risk management considerations and make an argument that collaborative writing can serve as one tool for resistance and empowerment when confronted with these challenges as librarians and library and information science scholars.

By Hayley Park and Negeen Aghassibake

Background

Writing about politically charged topics of the time forces scholars to weigh not only the costs and benefits of their work but also to consider risk management. Yet, what readers end up seeing is the published outcome, and rarely the decision-making process that scholars must navigate to get there. We recently wrote a  Library Trends publication titled “Empowering Immigrant Library Users Through Personal Data Literacy Programming in U.S. Public Libraries” (Park & Aghassibake, 2026), which sheds light on the impact of data surveillance on immigrants’ safety and recommends that public libraries play an active role in personal data literacy programming where possible. As we wrote the article during the first year of Donald Trump’s second presidency, we had to consider the meaning and boundaries of academic freedom and intellectual freedom, along with questions around safety and potential risks amidst intensified anti-immigrant rhetoric. 

This paper provides insight into the academic publication process, but it primarily contributes to the ongoing scholarly conversations around academic freedom in a time of anti-immigrant rhetoric and continued critical engagement in promoting library values and advocacy work. We argue that collaborative writing can serve as a form of resistance and a way to contend with uncertainty that enables scholars to critically engage in the process of navigating tensions, identifying values and power dynamics, and being vulnerable.

Key terms

For clarity, we offer descriptions of how we’re using related and often conflated terms. Academic freedom has a wide range of definitions across legal, political, and cultural contexts (Nelson, 2009). When we use the term “academic freedom,” we are largely aligning with the United Kingdom Education Reform Act of 1988 definition from Section 202 that refers to academics’ right to pursue research and lines of inquiry “without placing themselves in jeopardy of losing their jobs or privileges they may have at their institutions” (Education Reform Act 1988). We expand upon this definition to include freedom from institutional interference in academic activities with the purpose of reducing academic freedom.

We recognize that this definition varies from the 1940 Statement of Principles on Academic Freedom and Tenure by the American Association of University Professors (AAUP) that specifies academic freedom for faculty (1940). However, we are also in alignment with the following assertion in the AAUP Joint Statement on Faculty Status of College and University Librarians that recognizes academic librarians as members of the academic community and their need for academic freedom to conduct their work (American Association of University Professors, 1972): “Academic freedom is indispensable to librarians in their roles as teachers and researchers…as members of the academic community, librarians should have latitude in the exercise of their professional judgment within the library…” This also aligns with the ACRL Standards for Academic Librarians Without Faculty Status that says that “Academic Librarians are entitled to the protection of academic freedom” (Association of College & Research Libraries, 2011). Later in this article, we will discuss uneven protections of academic freedom for non-faculty academics in greater detail.

When we use the term “intellectual freedom,” we are generally referring to “the right of every individual to both seek and receive information from all points of view without restriction” (Garnar et al., 2022, p. 300). Intellectual freedom and academic freedom are related, though distinct, terms. Within our framework, academic freedom requires intellectual freedom to exist and is specific to institutions within the academy, whereas intellectual freedom is not restricted to academic life and applies to public life.

We also use the term “chilling effect” throughout this article. The definition we’re following here is based on Jonathan W. Penney’s explanation of chilling effect: “when a person, deterred by fear of some legal punishment or privacy harm, engages in self-censorship, that is, censors themselves and does not speak or engage in some activity, despite that activity being lawful or even desirable” (2022, pp. 1454-1455). As an example, within the context of this article, that could look like a researcher deciding not to pursue a politically charged research question due to a fear of harm to their career.

Another word that we refer to throughout this article is “safety.” When we use this word in this article, what we mean is safety from repercussions from our institutions and safety from impacts to our positions from within and outside of our institutions for exercising our academic freedom. This is not to say that we are opposed to critiques or debates, and as noted in the AAUP’s 1940 Statement of Principles on Academic Freedom and Tenure, we recognize that “the public may judge [our] profession and [our] institu­tion[s] by [our] utterances” (1940). Rather, it is the acknowledgment that fear of reprisal, such as job loss, and harm, such as stalking and individualized threats, has a chilling effect on academic freedom.

Setting the scene: writing in 2025

As we worked on our Library Trends article, the implementation of anti-immigration-related policies resulted in a stream of news covering the arrests of immigrants. In the educational context, attacks on diversity, equity, and inclusion (DEI) programs in higher education also led academic institutions to modify their language and programs, if not entirely remove DEI initiatives and positions, following Executive Order 14151, “Ending Radical And Wasteful Government DEI Programs And Preferencing” (The White House, 2025a).

In fact, several institutions of higher education, including Brown University, Columbia University, the University of Pennsylvania, the University of Virginia, among many more, agreed to Trump’s priority funding offer for accepting the compact that effectively threatens academic freedom and intellectual freedom on campus (Uglesbee et al., 2025).

Additionally, the library world was shocked when the Institute of Museum and Library Services (IMLS) was shut down by Executive Order 14217 (“Commencing the Reduction of the Federal Bureaucracy”) in February 2025 (The White House, 2025b), and its future remains uncertain (Landgraf, 2026). In this rapidly changing context, as a new PhD student, Hayley had to consider whether she could continue conducting immigrant-centered research. Most directly, the immediate suspension of the IMLS grant that funded her work meant the potential suspension of her salary. In addition, DEI-related terms and programs were targeted at public institutions, colleges and universities (Kim, 2025). Negeen felt the impact directly when an individual involved with anti-DEI organizations asked for her and her organization to be disciplined as a result of a program that she led.

The Trump administration released a list of more than 250 words deemed unacceptable in 2025, and the word “immigrants” takes space in the forbidden word list (Connelly, 2025). Assigning appropriate, representative keywords to a work is critical. There has been a record of information science literature emphasizing this point, from the classification aspect to developing a scholarly identity (Bowker & Starr, 1999; Inouye & McAlpine, 2019). This means that banning words such as “immigrants,” “advocacy,” and “equity” directly impede not only the effective discovery of a work but also the production and documentation of knowledge that enriches the scholarship on a topic.

Additionally, not being able to use specific words related to groups of people leads to an inability to discuss the harms that impact those groups. Political controversy can impede scientific research, leading to varying levels of self-censorship, from removing controversial words from research projects to leaving academia for an external position with greater job security (Kempner, 2009). In fact, drawing the direct parallel between Trump’s ban of seven ‘forbidden words’ from Center for Disease Control (CDC) during his first presidency — “vulnerable, entitlement, diversity, transgender, fetus, evidence-based, and science-based” (Sun & Eilperin, 2017), and the 2025 list (Connelly, 2025), Kronk and her colleagues analyzed the estimated impact of the government-imposed restriction of scientific language and found that the state and funder requirement of the removal of specific scientific terms could not only jeopardize research integrity as well as the communication of the findings (Kronk et al., 2025). Within the field of information, Kate Starbird, a Professor at the University of Washington who specializes in how information circulates in online spaces, has shared her personal account of being the subject of online harassment, her concerns about her and her team’s safety, and witnessing a chilling effect because of her scholarship on online mis-and disinformation related to the U.S. election (Starbird, 2023). Following the logic used by UC San Diego public health scientist Rebecca Fielding-Miller in an article about forbidden words (Sharma, 2025), if we are unable to use the word “immigrants” in our research, then we are unable to discuss the harms inflicted by the federal government on immigrants.

This move has forced scholars and institutions to reconsider their research directions and programs, leading to anticipatory self-censorship (The Lancet, 2025). This censorship of “‘disapproved subjects’” has led to a chilling effect on academics that has lasted well beyond the implementation of these restricted words (Blinder, 2026).

In this context, we had to take into consideration the changing political landscape affecting the conditions of our scholarly activities while writing about data and immigrants in libraries.

Positionality and privilege

We approached this experience and this subject with considerable privilege, given our positionalities. Hayley is a PhD student at a major research university, and Negeen is an academic librarian with permanent status at a different large research university. Both Hayley and Negeen generate scholarship in some form in order to progress in their careers. Hayley has some specific protections for graduate students as part of the University of Maryland (University of Maryland, n.d.), and Negeen has protections under Article 61 of the “Collective Bargaining Agreement By and Between the University of Washington and the Service Employees International Union Local 925 for Professional Libraries and Press Employees and Librarians” (2023). However, during the actual writing process, the application and the limits of these terms weren’t clear, as discussed in greater detail in the section below.

In addition, we are both women of color working at public institutions at a time when DEI-related activities in higher education are being scrutinized, and Hayley identifies as an immigrant working in this space at a time when immigrants are being targeted and attacked. We had to ask ourselves questions such as: What are the risks in writing this article? Can we weather those risks given our privileges? Are there risks we cannot see at the moment? Who may be surveilling our work? What is our responsibility given the privileges we do have? Are we overthinking this?

We experienced the tensions of these equally true realities and felt conflicting senses of vulnerability and privilege throughout the process of writing the original article and this one. We felt that one way to bring this conflict to the surface was through further collaborative reflection and writing. These conversations also helped us in our attempts to demystify academic freedom for ourselves and to consider the practical implications of exercising that freedom, given our positionalities.

In addition to the questions we asked above, we also uncovered questions related to how academic freedom applies to library workers across the field. Neither of us are faculty, and much of the language we found as we explored the literature on academic freedom related directly to faculty. Leebaw and Logsdon (2020) argue that academic freedom, in the context of academic librarians, needs further research. According to them, non-faculty academic librarians do not experience the same level of protection as faculty with tenure, and that one’s financial security and social identity impact their perception of their protection. This lack of a perception of safety and protection (as a result of not being faculty) potentially contributes to the chilling effect that some librarians may experience.

Throughout our research, the conclusion we came to is that the state of academic freedom for academic librarians is inconsistent across the field and institution-specific. Negeen’s union’s collective bargaining agreement (a legally binding contract), for instance, ensures academic freedom not only for librarians in the union, but also for non-librarian staff in the union:

The University of Washington recognizes Librarians’ and Libraries and Press professionals’ right to academic freedom and the right to examine and communicate ideas by any lawful means, even if such activities should generate hostility or pressure against the Librarians, Professional Libraries and Press employees, or the University (Collective Bargaining Agreement SEIU 925 UW Libraries and Press Union, 2023).

University of California librarians also won academic freedom rights in their collective bargaining agreement (Carrillo, 2019), whereas “academic freedom” is not mentioned in the Northwestern University Library union contract (Collective Bargaining Agreement Between Service Employees International Union, Local No. 73 and Northwestern University, 2023).

The status of academic freedom for PhD students is similarly inconsistent and unclear. While Hayley was unable to find academic freedom protections that directly applied to her, she did find academic freedom protections for University of Maryland School of Law students (and librarians) specifically: “Our commitment to academic freedom extends to all members of the law school community. We recognize the need for academic freedom for students and teachers, in their, at times overlapping, roles as scholars, educators, clinicians, administrators and librarians” (University of Maryland Francis King Carey School of Law, n.d.).

Chick (2025) finds that PhD students felt their academic freedom was under attack, especially when their topics were related to DEI issues. PhD students were being placed in a unique position of choosing between their commitment to equity and professional and academic risks. Chick further observes the doctoral students’ self-censorship as a consequence of the chilling effect of external threats to DEI initiatives and academic freedom in the context of educational innovation at Hispanic-serving institutions. Doerfler et al. (2021) also documented the experiences of scholars who have faced Internet-facilitated harassment, including doxxing, Zoom-bombing, and threats to funding cuts. Given the abundance of evidence in the literature, as well as our own anecdotal experiences, we knew we had to be thoughtful in how we made decisions.

Problem statement and significance

Scholarship on intellectual freedom and censorship under political and institutional pressure has a long history in LIS scholarship and practice. Yet, during our publication process, we found limited guidance on how to engage in academic writing about a topic that might be perceived as politically charged.

While Negeen has the protections in her union’s Collective Bargaining Agreement mentioned above (Collective Bargaining Agreement SEIU 925 UW Libraries and Press Union, 2023), the article suggests some limitations to those protections: “The expression of dissent and the attempt to produce change may not be carried out in ways that…disrupt the work of other University personnel.” The question of what is “disruptive” is undefined. Negeen was also unable to find specific guidance about how the university responds to threats to academic freedom, whether internally within the university or externally from the general public or government. This lack of clarity led to feelings of uncertainty about the boundaries of her academic freedom.

For Hayley, understanding the clear boundaries of her academic freedom was both straightforward and ambiguous. While the University of Maryland grants academic freedom as part of its intellectual freedom principle to all campus members, including students (University of Maryland, n.d.), there was no dedicated institutional document detailing how it applies to doctoral students, who operate in researcher, student, and teaching roles depending on funding and acceptance package, or the extent of protection and representation the university provides.

Similarly, while the AAUP provides a document on academic freedom and tenure that could be interpreted as extending protection to students in their learning, the scope and application of the agreement remain unclear (American Association of University Professors, 1940). This lack of clarity and practical guidance is further reflected in the absence of official institutional documents on this topic, from PhD orientation or doctoral program policy at the Graduate School level. This leaves doctoral students in a state of uncertainty as they exercise their intellectual freedom, navigate risks, and also complete their doctoral programs.

Some specific areas in need of further guidance include selecting publication venues, determining project scope, and author visibility to demystify the extension of author protection and academic freedom given to LIS PhD students and academic librarians, the detailed accounts of which we had difficulty locating in existing LIS literature. In response to this gap, and in the spirit of peer-sharing, we offer our decision-making process that shaped our topic selection, contextual scope, and the level of visibility we assumed as authors, including selecting venues and considering our safety (e.g., understanding potential threats, having a clearer sense of how institutional policies could be helpful or harmful) when advocating for immigrant rights, especially from perspectives of others in our academic (but not faculty) positions.

Our aim is to help other library scholars facing similar challenges think through the implications of exercising their freedoms and make informed decisions based on their own professional and personal circumstances. Our self-reflexive account of the immigrant-centered writing process contributes to the broader scholarship on academic freedom but also adds to the growing accounts of the lived experiences of library workers in navigating challenging political environments. In the following paragraphs, we will outline our publication experience, discuss issues of academic freedom and intellectual safety (especially in relation to intersectional identities and institutional privileges), and argue that collaborative writing is one form of resistance.

Context and timelines

The anti-immigrant policies around the time of our writing of the Library Trends article led to our discussions about academic freedom, which inspired the basis of this article. We submitted our Library Trends proposal in mid-December 2024, before Donald Trump took office, and our proposal broadly focused on data literacy programming for immigrants in public libraries, with some focus on privacy. We received notice of its acceptance on January 6, 2025, during a transitional period between the 2024 presidential election and the inauguration scheduled on January 20, 2026. When we began the actual writing process, Trump was inaugurated into the office, and on his first day, he signed about 34 immigration-related policies, 27 of which he revived from his first term, and seven additional policies (Roesenberg et al., 2025).

We recognized that the political and social context around immigration had shifted, and we realized that our initial conception of an article about data literacy programming for immigrants through public libraries with only a minimal focus on privacy was no longer sufficient. Rather, we needed to expand the focus to also include advocacy as well as personal data literacy as the foundation of privacy literacy against surveillance. For example, rather than conceptualizing data literacy as a necessary skill to better understand areas such as financial literacy and entrepreneurship, we recognized the emerging and more urgent need to make more accessible how personal data could be used against immigrants – for example, through programming that allows users to think through the implications of location sharing in mobile apps. This also included a shift from just thinking about programming to considering if and how advocacy plays a role in this work. We discussed the need to redirect some attention to contextualizing our recommendations in the history of advocacy in libraries and the recognition of increased surveillance of scholars and the general public, reflecting the historical and professional parallels in the rapidly changing political environment.

Our writing process spanned the entirety of 2025, with revisions and source updates responding to news and policy changes. The timeline of current events was closely intertwined with our publication timeline, prompting us to reflect on the challenges of writing about immigrant advocacy in academia amid a divisive political context. The article was published in February 2026.

Emerging concerns: Academic freedom and safety

Throughout the writing process, we confronted emerging concerns from the shifting political and social climate. These concerns forced us to face the public nature of our work, which also instilled a conflicting sense of both vulnerability and a renewed commitment to LIS values and led us to consider the issues around academic writing, its consequences, and the risks associated with it. Banning specific language, pressure on institutions, and threats to personal safety lead to a physical and psychological condition of a chilling effect in academia. Schauer (1978) discusses concerns over threats and safety, vulnerability, deciding when and how to speak about a topic. Initially conceived as a legal framework, Schauer captures the affective factors related to legal uncertainty, especially in relation to the exercise of First Amendment rights. The chilling effect here results in the avoidance of exercising one’s rightful freedom of speech due to fear of or uncertainty around potential legal consequences. Despite our best efforts, we often came up against the chilling effect and had to be intentional about not censoring ourselves in our writing and conversations.

Challenges against academic freedom are not new. What is common in these repeated challenges is a thread of patterns that shape and oppress the conditions of academic activities. Nelson (2009) outlines several threats to academic freedom, including authoritarian administration, unwarranted research oversight, political intolerance, legal threats, and claims of financial crisis. Many of these constructs describe the current sociopolitical landscape under the Trump administration, as discussed in this article. Threats against academics and their scholarly activities can be so severe that there are dedicated organizations such as the Scholars at Risk Network that provide support to those scholars globally (Adebayo, 2022).

One example in the United States is that of the experiences of Ricardo Dominguez, Professor of New Media, Performance Art, and a Principal Investigator at CALIT2 at the University of California, San Diego. Dominguez found himself at the center of the immigration and academic freedom debate for creating Transborder Immigrant Tool, which was a mobile application that was designed to help migrants find water caches and access and read poems. Dominguez was investigated for a potential misuse of funds and was under threat of termination from his position following pressure from three Republican members of Congress (Dominguez, 2014). The investigation found no evidence of a misuse of funds and he did not lose his job. However, this example illustrates how a work of academic and artistic expression can be used to try to criminalize a scholar, and how political and ideological differences (specifically anti-immigrant sentiments) can lead to attacks on scholars (Warren & Warren, 2011). These types of attacks on academic freedom and scholars’ livelihoods force scholars to negotiate between continuing their research and balancing real threats to individual and professional safety.

Decision-making process

To make informed decisions, we engaged in a collaborative assessment of our situation that involved discussions about scope and approach to writing, consultation with the editorial team and mentors, and regular team conversations to check in about our concerns.

Project scope and capturing the moment

As we worked on our article, we encountered a significant issue: an increasing number of news reports related to our topic, which raised concerns about academic freedom and intellectual safety. To begin, we had to decide the extent to which we wanted to document the current moment. Situating our inquiry in the current context that continued to unfold forced us to think about the relevance of not just the connection to our arguments for personal data literacy programming in public libraries, but also the details of the political conditions that continue the thread of advocacy in the history of libraries. Ultimately, we decided that we could not discuss non-citizen immigrants and personal data without also documenting current events. The decision to capture the current moment led to a discussion about source citing.

This led to more questions. The year 2025 was marked by political, legal, and policy shifts. Many cases that were in the news were still developing, and we would read updates as they progressed. It was difficult to get to a point where we felt like we could stop updating our literature review. We had cited cases that were, as of that moment, unresolved and still shifting. While the purpose was to situate our article within our specific context, it was hard to resist the temptation of a closure that we knew would never come. Ultimately, remembering the purpose of providing this background information (and quickly approaching deadlines) allowed us to pause.

Additionally, we believed it was important to capture the moment we found ourselves in through a less-than-traditional literature review and background that included newspaper articles and grey literature (such as reports from government agencies, policies from corporations, and presidential Executive Orders). We believed it was important to provide the context in which we were writing our article, and we also wanted to contribute to preserving this snapshot in the scholarly record. Given the likelihood that a news event as well as its source we referred to in the article would most likely change, we employed a few strategies to maintain the rigor of the publication. First, we made sure to include information about the specific time period that the writing took place in the narrative, along with an acknowledgement that the current landscape would likely look meaningfully different before even the final publication date. We also took snapshots of many sources by capturing their webpages using the Wayback Machine in case they are modified or taken down. Our hope was to maintain an accurate description of the context at the time of writing against the increasing instances of data and information erasure.

Risk assessment

Writing about advocacy and social justice and the exercising of intellectual freedom is one of the strengths of our field. Given the challenges against scholars at the time of writing, we had to consider the boundaries of exercising our intellectual freedom within our academic and institutional settings, especially given the public nature of our work. Other scholars have also felt compelled to change the direction of their work due to the threats of damaging emails or online bullying. Attacks following scholarly activities are also not unheard of in libraries.

In 2019, Professor Nicole Cooke and librarian Amy Koester hosted a conference called Defeating Bullies and Trolls in the Library: Developing Strategies to Protect our Rights and Personhood at the Skokie Public Library in Illinois. The conference featured several library scholars including Nicole Cooke, Stacy Collins, Kristin Lansdown, Amy Koester, Dianae Foote, Emily Knox, Jamie Naidoo, and Aimee Strittmatter, who spoke about bullying and harassment at both personal and institutional levels (Peet, 2019). This conference was built off of a panel discussion titled “Bullying, Trolling, and Doxxing, Oh My! Protecting our Advocacy and Public Discourse around Diversity and Social Justice,” which took place at the 2018 American Library Association Annual Conference in New Orleans and discussed library workers’ experiences of being bullied. While on that panel, Cooke and Miriam Sweeney specifically shared their experience with being harassed after Campus Reform (a conservative news source that covers higher education) published an article about their research on microaggressions in libraries (Peet, 2018).

Most recently, Oltmann and Dowell (2025) released a piece analyzing Professor Watchlist, a project created by the conservative organization Turning Point USA that surveils and “exposes” faculty for alleged discrimination of conservative students (Professor Watchlist, n.d.). They interviewed some faculty on the list and asked about its impact on their work. While some interviewees reported that their inclusion in Professor Watchlist was positive in the sense that they received support from their peers and institutions, they also noted concerns about its impact on academic freedom.

This all shows multi-layered tensions involved in the work. While researchers engage in important work, they also have to assess the potential risks of doing so against the risk of personal harm. The risk could ideally be mediated by institutions, yet, as Cooke and Sweeney specifically shared at the 2018 panel discussion, the response could either be delayed or lack an established protocol that provides an adequate level of protection, leaving the risk to be handled and experienced by the scholars themselves (Peet, 2018).

We experienced what we see as a fundamental tension between academic freedom and safety. That is, although we were granted academic freedom by our institutions, our work would be publicly available, so we were not necessarily guaranteed academic safety. Academic institutions, like many organizations, must also weigh risks and manage their public reputations. We grew concerned that threats to our reputation, institutional reputation and/or funding from external political forces could adversely impact our sense of academic freedom. These multilayered concerns stemmed from knowing how to identify threats to academic freedom but not enough about how to overcome these threats. To demystify the source of uncertainty, we consulted multiple members of our support network, beginning with the editorial team.

Editorial support

It takes a team to make an informed decision. Once we decided to situate our inquiry in the context of ongoing advocacy work in libraries, we needed to make sure our intention was aligned with the overall purpose and theme of the volume, “Data Literacy: Navigating the Shift from Hype to Reality (Chiewphasa, 2026). Additionally, Library Trends is published by the Johns Hopkins University Press (Hopkins Press, n.d.), and given the $800 million federal funding cuts the institution was facing in March 2025 (Daniels et al., 2025), we also felt the need to check in with our editor, Ben Chiewphasa. This led to honest and open dialogue about our reoriented research direction, as well as our concerns around the topic itself and our visibility, keywords, and the potential challenges to our work. These conversations helped us gain not only a sense of clarity but also support and being in community with other scholars.

Lessons learned

Throughout this experience, we learned lessons that we will carry forward and that we hope will make a meaningful contribution to conversations on academic freedom and safety for library workers and scholars.

Practical resources (and their limitations)

Having practical tools significantly reduces feelings of uncertainty and increases a sense of preparedness for potential threats. During the writing process for both the Library Trends article and this article, we discovered that some academic and non-academic institutions have provided practical guidelines to protect researchers’ safety.

In recognition of the common tactics of intimidation and harassment that some researchers face in their public-facing work, the Researcher Support Consortium created a series of resources to help researchers navigate potential risks and responses, and to equip them with coping strategies when attacked (Researcher Support Consortium, 2024b). The recommendations range from the removal of personal information on public websites, including institutional directories, to protecting online spaces with password requirements, and also preparing an organizational statement if necessary. The guide repeatedly emphasizes the importance of mitigating impact on one’s psychological well-being and physical safety, and recommends seeking out support in numbers and communicating with a support network. Along with these recommendations, the group also provides institutions with toolkits to protect and support their researchers, including a step-by-step guideline, a sample institutional policy, and certificates of confidentiality (Researcher Support Consortium, 2024a).

Several universities also provide researcher safety guidelines directly. The University of Colorado Boulder provides an extensive list of guidelines under Scholarship & Safety, including a checklist based on professional positionality (e.g., researcher, administrator) in the context of the event (Academic Affairs at the University of Colorado Boulder, n.d.). Similarly, York St. John University clearly addresses the term “researcher vulnerability” and provides institutional guidance on addressing physical and psychological vulnerabilities associated with research (York St. John University, n.d.). Both examples of institutional guidance include different types of risks associated with research, not only online harassment and physical harms but also the psychological aspects; this includes prolonged engagement with sensitive research topics and the potential to experience vicarious trauma.

While these documents provide a starting point for protecting oneself against potential attacks, they are few in number. The recommendations, while calling for institutional support, primarily ask individual researchers to take protective measures, thereby adding additional psychological and, at times, financial burden (such as subscribing to personal data removal services). Additionally, these guidelines may not cover all categories of scholars at an institution (e.g., PhD students, teaching faculty, staff scientists, academic librarians). We recognize the value of these tools and also acknowledge that they have limitations, and we suggest that institutions take steps to develop more comprehensive safety plans for all types of scholars.

Power of collaborative writing

One of the most important lessons we learned from writing about immigrants, data literacy, and surveillance in 2025 is that collaboration and care are critical to our decision-making process as well as our well-being as we work through our concerns. In addition to the typical challenges we expected when writing on our topic, we encountered new challenges through renewed anti-immigrant sentiments and threats to academic freedom. These issues impacted our psychological states more than we had initially anticipated. However, through our collaborative writing process and our weekly check-ins, we provided support for one another and worked through our concerns collectively and while critically interrogating the unfolding events. Exercising our library values and putting them into practice felt empowering and generative during a time that was otherwise demoralizing.

Additionally, we were grateful for the support we received from our editorial team. We were able to be open with them about our concerns and fears, and we had opportunities for low-stakes exploration of various options to minimize our exposure. We also received institutional support from our advisors and supervisors. These were key ingredients in our decision-making process, and we acknowledge that they were privileges not afforded to all academics and advocates writing in this space.

One of the most important aspects of our writing process was acknowledging the moment in history in the article itself. This helped ground us and provide context to our conversations about how to move forward with the article. Furthermore, it was a reminder that all articles are written under a particular context, whether stated or unstated, and we will consider including the social and political context in future articles we write.

Another lesson we learned, which is not a unique lesson but needs to be considered individually, was our decision on whether or not to use our real names and institutions in our publication. We had several discussions related to this issue, especially after one of us was targeted at work. Ultimately, we learned a lesson that other scholars may have already learned: we could either be completely anonymous or very visible.

Conclusion

We hope that sharing our experience and situating it in the wider context of academic freedom and intellectual freedom can contribute to discussions about the protections that library workers have (and lack) when exercising their freedoms. We felt that it was important to discuss not only the abstract, theoretical issues related to this topic, but also our lived experiences.

We are grateful to all of the scholars working on academic freedom, particularly the work of Leebaw and Logsdon (2020) and Nicole Cooke, Stacy Collins, Kristin Lansdown, Amy Koester, Dianae Foote, Emily Knox, Jamie Naidoo, and Aimee Strittmatter (Peet, 2019). One area of future research is to update the data on the state of academic freedom in libraries and for PhD students today given the rise in new union contracts that we found throughout our research. Ultimately, a more systematic approach to analyzing union contracts and institutional policies across libraries and doctoral programs will better prepare library workers and PhD students (and graduate students more broadly) to fight for the academic freedoms that protect faculty (while still recognizing its limitations).

One of the many inevitable consequences in writing about a topic that was personally and professionally challenging was that we experienced a range of emotions as we wrote: fear of being targeted, anger at the persecution of immigrants, and occasional moments of despair. We recognized the ways in which these emotions could lead to self-censorship and the watering down of the realities of that moment in history. This led to regular conversations about how we were framing current events and the urgency in our tone throughout the article, which needed to be balanced with practical suggestions for readers. This was a negotiation neither of us had experienced in writing an academic article, and our discussions were essential in determining our next steps, along with the support of our editor and peer reviewers.

Of course, we recognize that while powerful, writing is not a substitute for other actions. However, our writing partnership has helped us push against feelings of fear and fatalism, and it has been a way to connect with others who also care about these issues. We hope that readers are inspired to find community through writing and use it as one tool (even if a small tool) of resistance.

Acknowledgments

We are grateful to our editor, Pam Lach, and our reviewers, Brea McQueen and Shawn(ta) Smith-Cruz, for their support in guiding us through this process. At a time when we are all experiencing pressures from many directions, their generosity, energy, and care is even more meaningful. This article is better having been shaped by their wisdom and thoughtfulness. We are also thankful for our partnership and to LIS scholars for motivating us to keep on writing.

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Warren, L., & Warren, S. (2011). The Art of Crossing Borders: Migrant Rights and Academic Freedom. Boom, 1(4), 26–30. https://doi.org/10.1525/boom.2011.1.4.26

York St John University. (n.d.). Researcher vulnerability. Retrieved February 14, 2026, from http://www.yorksj.ac.uk/policies-and-documents/research/ethics-and-integrity/roles-and-responsibilities/researcher-vulnerability/

I am pleased to share that I have been awarded an Internal Research and Development Grant from the Office of Enterprise Research and Innovation at Old Dominion University in the amount of $50,000, beginning July 1, 2026. The project, titled “Trust-Centered AI Avatars for Accessible Telehealth Support Among Unhoused Populations,” is co-led with Drs. Ginger Watson and Tina Gustin.

This project proposes to improve healthcare access for vulnerable and underserved populations by leveraging, enhancing, and evaluating an NSF-funded telehealth kiosk, TeleHSupport, to improve usability through an audio interface and AI-powered interactive avatar. TeleHSupport is a secure, self-service kiosk enabling individuals with limited access to traditional healthcare to receive reliable AI-generated health information and guidance. It delivers safe, evidence-based guidance on chronic disease through text-based conversational AI, with clinically validated content curated by advanced practice nursing faculty. Individuals select symptoms through step-by-step prompts, which are processed using a retrieval-augmented generation approach to assess symptom severity and likely conditions. The kiosk provides consistent guidance while prioritizing patient safety. Ongoing deployment and focus group studies have identified low general and health literacy, and trust as barriers that limit adoption and effective use.To address these challenges, the proposed work integrates voice recognition with a human-AI avatar agent to enable an agentic AI framework, support multimodal, conversational interaction, reduce reliance on text, and foster trust, engagement, and comprehension. Leveraging generative AI, large language models (LLMs), Retrieval-Augmented Generation (RAG), human-computer interaction (HCI), and modeling and simulation, the project will design, implement, and evaluate avatar-mediated interactions tailored to the needs of unhoused populations.

The work builds directly on my prior NSF investments and represents a critical next step toward scalable, trustworthy AI-enabled health support systems deployable beyond Hampton Roads. The proposed study will provide usability and adoption data critical for larger-scale deployment within and beyond Hampton Roads.

Faryaneh Poursardar (@Faryane)

*Picture: Patient signed the consent form.

Community voting is now open for the 2026 Virtual DLF Forum! Community voting lets DLF and the Program Committee know which proposals resonate with our community. Results are weighed when developing the final event programs. Anyone may participate, and you may vote for as many proposals as you’d like, but each one once.

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The post Community Voting Now Open for the 2026 Virtual DLF Forum appeared first on DLF.

In one of our very first AI Learning Labs projects, we are teaming up to explore how to design a chatbot that draws on reliable science and systemic social analysis

The post How can we build a responsible climate chatbot? first appeared on Open Knowledge Blog.

GitLab announced layoffs today. They don't state how many people are affected, but honestly I find this really frustrating for several reasons:

1. This is the one time where they could have won by doing relatively nothing. GitHub is having big outages on a daily cadence. All they have to do is market themselves as "we're the stable one" and maybe add tooling to run your existing GitHub Actions in GitLab to make the transition easier. They could have won so hard it's not even funny because GitLab makes it trivial to host it yourself.
2. This is yet another case of "the stock price has gone down but we don't want to look bad to investors so we'll say that AI is going to help us more". I'm increasingly skeptical of this claim, but it's what makes the company look good to the people with the money sooo...
3. They claim that one of their main goals is "Speed with Quality". Usually this is a "of two, pick one" type of scenario. I shudder to think what may happen when GitLab turns into a feature factory powered by something on the lines of Protos.

Maybe GitLab did need to trim the fat, maybe they will come out of this stronger, but damn I just can't help but think about a world where they could have won without AI and just by being more stable than GitHub.

Numa

One small problem with that: what you are suggesting makes sense. We live in clown world with clown world logic. Why would we be allowed to have things that make sense in clown world?

Also yes, I do know that clowning is actually a very difficult art to pull off correctly. Humor is one of the most difficult things on the planet because if you do it wrong you offend people. People that are offended generally aren't people that are laughing. As a character that largely amounts to being a jaded contrarian comedian this is something that comes up a lot when planning what I say.

Also maybe I'm just oversensitive to it at this point, but the layoff announcement really reads like Claude Opus output. What a fuckin' world.

Aoi

I don't want to live on this planet anymore.

Apropos of nothing, I'm really enjoying my experimentation with Tangled. More to come soon when I have more to say.

This is not an OpenRefine tutorial. As I was trying to explain the process I took to read and code my sabbatical research, I realized that it could be best described as getting galaxy brained with OpenRefine and pandoc. I thought it might make an interesting blog post – less as a how-to or advice and more as an example of what real-world problem-solving looks like in libraries, or at least in my life. The alternative title could be “What It’s Like Inside My Weird Brain.”

The Problem

I got 267 responses to my 2024 survey (if you’re wondering why outputs haven’t yet been published, that is one contributing factor). Because I was trying to understand morale and migration, I’d allowed for free text responses along with controlled value fields. And, if you’re not aware, people have a lot of feelings about migrations. As a researcher, this is great!

These long textual responses were a great way of developing a fuller understanding of why a person answered the way they did. But when it came to assessing the data, this meant I would need to code all 267 responses or at least review and see if anything could be coded. Otherwise, I would be losing a ton of information in the statistical portions of analysis. And reading these in a spreadsheet client sounded miserable. I experimented with importing the sheet into NVivo, which seemed like a natural fit for coding, but didn’t feel I could properly map in my data. That might well be a skill issue, I’ve only used NVivo for interview transcripts, which are a completely different thing.

The Approach

What I needed was to be able to turn each response into its own pages. I’d set up the question in the output so that I had context for each answer. For example:

Which ILS did you previously use? Sirsi Dynix Symphony

My first thought: “oh, I could do this with Python.” The original Qualtrics export was in CSV. I’ve processed CSVs in Python for ages. I could output each response in markdown and use pandoc to transform them into a PDF. I then planned to read that PDF with my ReMarkable and take notes both on the sheet and in a running document.

The second half worked great. Simple markdown is simple. Pandoc is easy enough to use, although I did have to fuss around a bit to get a font I liked (and on my Linux machine). And ReMarkable remains the best way I have of reading and taking notes on PDFs.

But when I started messing with it in Python, I quickly got annoyed. It was doable, but it wasn’t easy. I felt like there should be something more straightforward.

And that’s when I remembered OpenRefine templates. Just before my sabbatical started, I’d been working on a project where I turned a spreadsheet into JSON through OpenRefine’s templating system. It’s a batch process I do once a year and I’ve put energy into, well, refining it. Because I only do it once a year, I also have enough documentation to go from my annual “wait how does this work again?” to getting it done. I imported the survey CSV into OpenRefine and started tackling it.

The Steps

When you go to the export templating area, OpenRefine helpfully outputs all existing fields in a sample template. So even though I’d have to hack it to pieces, I had a startiing list to work from. I kept my key nearby so I had a clue what Q22 might mean. I used the value split, replace, or forNonBlank processes on each line, grouping some together, and previewed results periodically to make sure I was on the right track.

It wasn’t the fastest thing I’ve ever done, but it took less than an hour to write, revise, and put out a final export. Just so there’s something useful in this post, this is the template I ended up with.

## {{cells["ResponseId"].value}}

**How many years have you worked in libraries?** {{cells["Q4"].value}}

**What kind of library do you work in?** {{cells["Q5"].value}} {{forNonBlank(cells["Q5_6_TEXT"],c,":" + c.value,"")}}

**How is your job classified?** {{cells["Q6"].value}} {{forNonBlank(cells["Q6_3_TEXT"],c,":" + c.value,"")}}

**How would you describe your job status?** {{cells["Q7"].value}} {{forNonBlank(cells["Q7_3_TEXT"],c,":" + c.value,"")}}

**If you regularly use the ILS (back-end system), what kind of tasks do you use it to perform? (choose all that apply)** {{cells["Q8"].value.replace(",",", ")}}

{{forNonBlank(cells["Q8_10_TEXT"],c,":" + c.value,"")}}

**How much time each week do you estimate that you spend using the ILS?** {{cells["Q9"].value}}

**If you regularly use your library's public online catalog or discovery platform as part of your work, which kind of tasks do you perform or support using these?** {{cells["Q10"].value.replace(",",", ")}} {{forNonBlank(cells["Q10_10_TEXT"],c,":" + c.value,"")}}

**How much time each week do you estimate that you spend using the online catalog and/or discovery platform?** {{(cells["Q11"].value)}}

**Which ILS did you previously use?** {{cells["Q13"].value.replace(",",", ")}} {{forNonBlank(cells["Q13_12_TEXT"],c,":" + c.value,"")}}

**Which ILS do you now use?** {{cells["Q14"].value.replace(",",", ")}} {{forNonBlank(cells["Q14_10_TEXT"],c,":" + c.value,"")}}

**When did your ILS migration complete? (please specify the year or month/year):** {{cells["Q15"].value}}

**About how many years had you (personally, not your institution) used the previous system?** {{cells["Q16"].value}}

**How would you describe degree to which the workflows of your primary job responsibilities have changed since the migration:** {{cells["Q18"].value.replace(",",", ")}} {{forNonBlank(cells["Q18_4_TEXT"],c,":" + c.value,"")}}

**How do you feel overall about any changes to your workflows?** {{cells["Q19"].value.replace(",",", ")}} {{forNonBlank(cells["Q19_4_TEXT"],c,":" + c.value,"")}}

**Please describe some challenges you experienced during the first 6 months post-migration:** {{cells["Q20"].value}}

**Did these challenges continue to impact your work at the 18-month mark post-migration (as well as you remember)?** {{cells["Q21"].value}}

**How were these challenges resolved?** {{cells["Q22"].value.replace(",",", ")}} {{forNonBlank(cells["Q22_6_TEXT"],c,":" + c.value,"")}}

**If you have any comments or reflections on either the resolution or on challenges you still experience, please provide them below:** {{cells["Q23"].value}}

**Were there aspects of your library's new ILS which substantially improved your ability to get things done or were features you'd always wanted?** {{cells["Q24"].value}}
	
**Please describe any aspects of your new ILS which substantially improved your ability to get things done or were features you'd always wanted:** {{cells["Q25"].value}}

**How would you summarize the impact of the ILS migration on your ability to complete your work as of today:** {{cells["Q26"].value}}

**Do you feel that your direct supervisor has/had reasonable expectations of the work you'd accomplish during the first 6 months post-migration?** {{cells["Q27"].value}}

**How would you describe your unit's current morale compared to unit morale prior to the migration:** {{cells["Q28"].value}}

**Is there anything you'd like to add to your assessment of the migration's impact on your unit's morale:** {{cells["Q29"].value}}

**How would you describe your own current morale compared your morale prior to the migration:** {{cells["Q30"].value}}

**Is there anything you'd like to add to your assessment of the migration's impact on your own morale:** {{cells["Q31"].value}}

Because there’s a lot of personal stuff in it, I can’t share a full sample response, but it came out like:

## R_7M9zOZyDpTDwXnj

**How many years have you worked in libraries?** more than 10 years

**What kind of library do you work in?** Academic 

**How is your job classified?** Librarian 

**How would you describe your job status?** Full-time 

**If you regularly use the ILS (back-end system), what kind of tasks do you use it to perform? (choose all that apply)** Cataloging

Pandoc was a bit harder, perhaps partly due to some settings in Linux, but ended up being something like:

pandoc markdown_survey_export.md --pdf-engine=xelatex -V 'mainfont:DejaVuSans.ttf' -V 'mainfontoptions:Extension=.ttf, UprightFont=*, BoldFont=*-Bold' -o Survey_Responses.pdf

Results

The resulting PDF was 231 pages long. It was well-formatted reading with one header for each entry and bolded questions. I spent a lot of time reading through it (twice), noting factors described in the free-text, condensing them into a codebook, and then applying appropriate terms to each entry.

Discussion

Ok, the header is a little tongue in cheek. This isn’t an article. I should be revising a nearly-complete article right now.

When I was trying to explain this, I realized it’s a good example of how I’ve experienced tech librarianship, from my early metadata days to things I do now. Can I write scripts? Yes, I do so all the time. I wrote a small processing script in Python earlier this week (last week now) and I can’t even recall that it was for because it was so ordinary and took maybe 10 minutes to get right. But I also use the things I know. Sometimes it’s easier to clean up a CSV by opening it in a text editor and performing a series of regular expression find and replace operations. Sometimes I use OpenRefine to … create PDFs? Sometimes I write journal articles and blog posts in Joplin.¹

It’s possible to take this too far. Can you share an image or dataset via Word or Powerpoint? Yes. But please don’t. I’d encourage those who aren’t as comfortable with these whacky decisions to feel your way out and assess outputs using the following criteria:

• Is it in the appropriate format?
• Is there a loss in quality?

…I was trying to think of a third to be traditional, but this is all that came to mind (I’m down to update it if someone thinks of a third or fourth). I’d say a loss in usability, but I think appropriate format (image should be JPG/PNG/TIFF/BMP/etc., dataset should be some kind of dataset format) and quality cover that.

Should I end this on an encouraging note? If you do this already, you’re not as weird as you think you are. If you’re not doing things because you don’t have time to buckle into the “right” tools, are there things which let you get the output you need while, taps the sign, don’t take the result off the rails too? Give it a go!

There sure is a lot of noise out there about so-called “artificial intelligence.” (I won’t bother with the scare quotes through the whole post, but it’s worth noting: “AI” is a term that’s been in use, mostly for marketing, since the 1950s (here is the term’s original usage), and as such, “resists definition because it is continually reappropriated by people to mean different things.”) I’m a little sorry to add to it, but there are many people out there being pressured to use AI products, who I think should be informed of the risks and allowed to decide whether to use them or not. My primary focus, here, will be “generative AI,” the chatbots that use huge corpora of text and images to generate outputs that appear novel, and my approach will be largely practical; I’ll save ethical and environmental concerns for the last section, and mostly link to better sources than this post. I also limit myself to work applications — so I removed a section about chatbots’ effects on mental health, despite my belief that their sycophancy could potentially cause problems at work.

Contents of this post

To save you some scrolling in case you’re here for something specific.

Why there’s no incentive for management to require AI usage:

• The benefits are usually negligible, but often nonexistent and sometimes even negative (and this is/will be true both Now and in The Future)

Why people deserve informed consent before using these products:

• Harms to individual users
  • AI usage damages critical thinking and encourages cognitive surrender
  • AI products encourage dependence and decrease self-confidence
  • AI usage hinders skill formation and learning on novel tasks
  • AI usage erodes expertise and damages performance even on familiar tasks
  • AI usage amplifies individuals’ biases and flaws in judgment, in ways that are invisible to them (which is a problem, because bias is baked into these products)

An additional AI policy suggestion:

• Sending uncorrected “workslop” to someone else is always inappropriate, because
  • AI outputs can be hard to parse
  • AI outputs must be understood to be corrected (and they always need to be corrected)

And last, but in a just world, not least:

• Ethics and the environment (mostly a list of resources)

The benefits are usually negligible, but often nonexistent and sometimes even negative

Let’s cover this first, since it’s the part management is most likely to care about—and because it’s counter to everything the marketers are telling us, I have citations—AI does not improve individual performance in any meaningful way. Occasionally, for some tasks, there are small improvements, but when weighed against the near certainty that these products will increase in price, the potential for catastrophic mistakes, and the likelihood of damage to the individuals using these products (which is the bulk of this post), I argue that management has no incentive to force AI use on workers.

Now

According to MIT’s The State of AI in Business in 2025 (emphasis mine), “Despite $30-40 billion in enterprise investment into GenAI, this report uncovers a surprising result in that 95% of organizations are getting zero return.”

An uncomfortable number of publications about worker productivity focus on qualitative assessments:

• From the Wall Street Journal, “Two-thirds of nonmanagement staffers said they saved less than two hours a week or no time at all with AI. More than 40% of executives, in contrast, said the technology saved them more than eight hours of work a week.”
• Also WSJ, summarizing a different study: “A new report from the business-software company Workday goes so far as to call frustrations with the technology an ‘AI tax’ on productivity. Though 85% of the roughly 1,600 employees it surveyed reported saving one to seven hours a week by using AI, much of the time was offset by having to correct errors and rework AI-generated content.”
• From upwork, “Nearly half (47%) of employees using AI say they have no idea how to achieve the productivity gains their employers expect, and 77% say these tools have actually decreased their productivity and added to their workload.” (The same executive summary says 71% of workers are burnt out, which… yeah, that sounds right.)

On the quantitative front:

• For programmers, we have a measured 19% slow-down in development speed in early 2025. Notably, the developers thought they had improved by 20%, which suggests self-reported gains from AI should be considered optimistic, at best.
• Uplevel (Uplevel’s report download page, or here’s a summary if you aren’t prepared to share an email address) studied 800 programmers with and without Copilot assistance and found that, while their speed did not change, programmers using AI pushed 41% more bugs.
• “A UK government department’s three-month trial of Microsoft’s M365 Copilot has revealed no discernible gain in productivity – speeding up some tasks yet making others slower due to lower quality outputs.” From The Register.
• What it says on the tin: AI can cost more than workers now, by Madison Mills, April 2026.

The future

I can almost hear the AI fans shouting, “But that’s 2025! We’re looking at the future!” And, you know what? That’s fair. Let’s talk about the future.

A great deal of the hype is based on a simple bet: that these products, which are currently pretty mid, will improve over time. But it’s a bad bet. Last September researchers at OpenAI proved that there is no way to create a large language model that doesn’t hallucinate. (I linked you to a summary, there, but if you like mathematical proofs, here is the original OpenAI article.) These are inherently probabilistic technologies, which means their output cannot be constrained to what we commonly refer to as “the truth.” No amount of engineering, scaling, or guardrails can prevent these machines from lying to us. We’re also running out of scaling opportunities; the internet, though large, is finite. A second bet, that the LLMs can feed each other, leading to a “superintelligence,” has also been shot down by mathematical proof. There’s a convergence point beyond which they don’t improve and actually get significantly worse. (I’ll be honest: mathematical proofs aren’t my thing. This post is not at all restrained in its rhetoric, but it does a good job explaining what’s going on in the paper.)

If a singularity is coming, it won’t be from this branch of technology.

So, OK, genAI won’t really get better over time, unless we pour a whole lot more human-generated data into it; and even then, there are limits. The companies can make little refinements, but we’re stuck with hallucinations and they need to stop slurping up everything, lest their products degrade further in quality.

Conclusion: there is no benefit to the enterprise in pushing individual contributors to use AI.

(Question: do I need a follow-up post about how silly the idea of replacing workers with AI is? Klarna may be one of the 55% of businesses that admit they shouldn’t have cut workforce in place of AI — and that’s just the ones willing to admit to being wrong. Even for replacing freelancers, AI’s a no-go.)

Harms to individual users

I’d like to believe management would care equally about this, and to be fair, a lot of managers will: good managers want their employees to be successful, even after they’ve moved on. Above a certain level, though, it seems like individuals stop mattering. So at this point, I’m talking mostly to line managers and to individuals who need to decide whether to implement genAI products in their own work.

Not all of these harms are equally applicable to everybody, but I believe they should all be shared with anyone who is thinking of using these products for their work.

AI usage damages critical thinking and encourages cognitive surrender

• “While LLMs offer immediate convenience, our findings highlight potential cognitive costs. Over four months, LLM users consistently underperformed at neural, linguistic, and behavioral levels.” – Your brain on ChatGPT: Accumulation of cognitive debt when using an ai assistant for essay writing task, by Kosmyna et. al., arXiv preprint, 2025.
• Critical thinking is inversely correlated with trust in AI systems. – The Impact of Generative AI on Critical Thinking: Self-Reported Reductions in Cognitive Effort and Confidence Effects From a Survey of Knowledge Workers, by Lee et. al., Proceedings of the CHI Conference on Human Factors in Computing Systems, April 2025.
• “Across studies, participants with higher trust in AI and lower need for cognition and fluid intelligence showed greater [cognitive] surrender.” – Thinking—Fast, Slow, and Artificial: How AI is Reshaping Human Reasoning and the Rise of Cognitive Surrender, by Steven D. Shaw and Gideon Nave, The Wharton School Research Paper, February 2026.
• This interview’s summary of the next paper is good: “all users show a significant inability to assess their performance accurately when using ChatGPT. In fact, across the board, people overestimated their performance. … ‘We found that when it comes to AI, the Dunning-Kruger Effect vanishes. In fact, what’s really surprising is that higher AI literacy brings more overconfidence,’ says Professor Robin Welsch. ‘We would expect people who are AI literate to not only be a bit better at interacting with AI systems, but also at judging their performance with those systems – but this was not the case.’” – AI makes you smarter but none the wiser: The disconnect between performance and metacognition, by Fernandes et. al., Computers in Human Behavior, February 2026.

AI products encourage dependence and decrease self-confidence

• This one’s interesting because this person is pro-AI. But I found this quote telling: “If you think about it, that 30-second wait for AI responses can be seen as a variable ratio schedule — Random rewards delivered at unpredictable intervals — the same psychological pattern that makes slot machines, social media, and mobile games addictive.” – Vibe Coding Is Creating Braindead Coders, by Namanyay Goel, Blog post, September 2025
• This one will hurt to read, a bit, if you’re already using these products, and I’m sorry. It’s a good warning to the folks who aren’t, yet, and it comes with citations. Generative AI runs on gambling addiction — just one more prompt, bro!, by David Gerard, June 2025.
• “The study included 1,923 online adult participants from the United States and Canada who were told to use commercially available AI programs to complete 10 simulated work tasks, such as developing plans with incomplete or evolving information, interpreting ambiguous data, and articulating reasoning for strategic decisions.
  
  “After the tasks, 58% of the participants agreed that AI ‘did most of the thinking’ to complete the work, especially in activities related to planning or sequencing. Those participants also reported reduced confidence in their own independent reasoning, lesser perceived ownership of ideas, and making trade-offs between task speed and depth of thought.” (quote from this summary) Furthermore, “Greater prompt dependence and lower override frequency were associated with reduced self-reported confidence in independent reasoning” – Generative Artificial Intelligence Reliance and Executive Function Attenuation: Behavioral Evidence of Cognitive Offload in High-Use Adults, by Sarah Baldeo in Technology, Mind, and Behavior, 2026.

AI usage hinders skill formation and learning on novel tasks

• “We find that AI use impairs conceptual understanding, code reading, and debugging abilities, without delivering significant efficiency gains on average.” – How AI Impacts Skill Formation, by Judy Hanwen Shen and Alex Tamkin, January 2026.
• LLM summaries produce shallower learning and understanding than a traditional web search. – Experimental evidence of the effects of large language models versus web search on depth of learning, by Shiri Melumad and Jin Ho Yun, October 2025.
• “The analysis of these examples suggests that automation can result in the loss of expertise due to reduced opportunities for learning from deliberate practice and experienced colleagues, and from working on progressively more complex tasks.” – The Impact of Artificial Intelligence on Expertise Development: Implications for HRD, by Alexandre Ardichvili, Advances in Developing Human Resources, 2022.

AI usage erodes expertise and damages performance even on familiar tasks

• Using AI assistance hurts performance on both arithmetic and reading tasks as soon as the AI is removed. – AI Assistance Reduces Persistence and Hurts Independent Performance (website, arXiv preprint), by Liu et. al., arXiv preprint, April 2026.
• “[O]ver the course of six months, clinicians became over-reliant on AI recommendations and became themselves ‘less motivated, less focused, and less responsible when making cognitive decisions without AI assistance.'” – New Study Suggests Using AI Made Doctors Less Skilled at Spotting Cancer, by Miranda Jeyaretnam, Time, August 2025 (and here is the study it references)

AI usage amplifies individuals’ biases and flaws in judgment, in ways that are invisible to them

• “AI systems can exhibit biased judgements in domains ranging from perception to emotion. Here, in a series of experiments (n = 1,401 participants), we reveal a feedback loop where human–AI interactions alter processes underlying human perceptual, emotional and social judgements, subsequently amplifying biases in humans. This amplification is significantly greater than that observed in interactions between humans, due to both the tendency of AI systems to amplify biases and the way humans perceive AI systems. Participants are often unaware of the extent of the AI’s influence, rendering them more susceptible to it.” – How human–AI feedback loops alter human perceptual, emotional and social judgements, by Moshe Glickman and Tali Sharot, Nature Human Behaviour, volume 9, pages 345–359 (2025).
• Autocomplete affects the answers people give, and most concerningly, “the people in the study didn’t tend to think the AI autocomplete suggestions were biased or to notice that they had changed their own thinking on an issue in the course of the study.” – AI autocomplete doesn’t just change how you write. It changes how you think, by Claire Cameron, Scientific American, March 2026, extending a 2023 study by Jakesh et. al.
• The bullets above are a problem because these models are demonstrably biased.
  • “Here, we advance studies of generative language model bias by considering a broader set of natural use cases via open-ended prompting… In this setting, we find that across 500,000 observations, generated outputs from the base models of five publicly available language models (ChatGPT 3.5, ChatGPT 4, Claude 2.0, Llama 2, and PaLM 2) are more likely to omit characters with minoritized race, gender, and/or sexual orientation identities compared to reported levels in the U.S. Census, or relegate them to subordinated roles as opposed to dominant ones. We also document patterns of stereotyping across language model–generated outputs with the potential to disproportionately affect minoritized individuals.” – Intersectional biases in narratives produced by open-ended prompting of generative language models, by Shieh et. al., Nature Communication, 2026.
  • “Essays attributed to Black students received more praise and encouragement, sometimes emphasizing leadership or power. … Essays labeled as written by Hispanic students or English learners were more likely to trigger corrections about grammar and ‘proper’ English. When the student was identified as white, the feedback more often focused on argument structure, evidence and clarity — the kinds of comments that can push writers to strengthen their ideas. The AI models addressed female students more affectionately and used more first-person pronouns.” AI gives more praise, less criticism to Black students: Identical essays get different feedback in Stanford study, by Jill Barshay, April 2026; original article preprint.
  • “Both large language models significantly underestimated disability in a population of people, and linguistic analysis showed that descriptions of people, patients, and athletes with a disability were generated as having significantly fewer favorable qualities and significantly more limitations than people without a disability in both ChatGPT and Gemini.” – Disability Ethics and Education in the Age of Artificial Intelligence: Identifying Ability Bias in ChatGPT and Gemini, by Urbina et. al., Archives of Physical Medicine and Rehabilitation, January 2025.

Sending uncorrected “workslop” to someone else is always inappropriate

Executive function theft (digression: this is such a great post; it really clarified something I’d observed but hadn’t previously had a cohesive description for) has been a problem in workplaces since time immemorial, but we’re already seeing genAI products making it worse, with huge productivity costs to workers receiving “workslop” products. (“Workslop” definition: “AI generated work content that masquerades as good work, but lacks the substance to meaningfully advance a given task.”) As Harvard Business Review summarizes the issue: “When coworkers receive workslop, they are often required to
take on the burden of decoding the content, inferring missed or false context. A cascade of effortful and complex decision-making processes may follow, including rework and uncomfortable exchanges with colleagues.”

I have to wonder if this is some of the time savings reported by executives: they do a little prompting, send off the output, and one of their employees is stuck doing the difficult part. Anyway…

Besides the obvious, “let people opt out,” a second concrete proposal I made for my workplace’s AI Strategy was “AI products should not be used to generate outputs that can’t be tested and corrected by the team using the product.” (Even that is too broad; they should be tested and corrected by the person using the product.)

• An example of an acceptable use: someone needs a ton of data parsed, and Excel is choking on the volume; they use a genAI product, which creates a pandas script to parse the data for them. The code itself isn’t the point and likely won’t be used beyond that session. They spot check the output of the script with smaller portions of their Excel file, to make sure it isn’t totally off base, before using it to make decisions.
• An example of an unacceptable use: someone uses a genAI product to create an application and asks the technology group to deploy it. The technology group, if they’re doing due diligence, now has to read through and probably correct that code. (So this person has just generated a ton of work for another team, when instead they should have worked with the experts on that team, who likely would have been able to code something simpler, or at least something they understand well enough to deploy safely.)
• Acceptable: someone uses a genAI product to generate a bulleted list of some sort, within their own knowledge domain. They go through, add necessary context, remove duplicates and fix weird phrasing. When it’s clean, they send it off to their task force, to add to a final report.
• Unacceptable: someone uses a genAI product to generate a bulleted list about something they don’t fully understand. They send it off to their task force uncorrected, and someone else will now have to do the work to fix it.

AI outputs can be hard to parse

I genuinely don’t know how universal this is. I can find a lot of neurodivergent folks talking about the difficulties they have with AI output, so I know at least a subset of people have a really difficult time with this. (Of course, others are capitalizing on it; but even there, at least some of what’s happening is full rewrites.)

I will say, for myself, that I do not have a diagnosis of autism or ADHD, and I slide right off AI-generated text. Have you ever been reading a book, or you thought you were, and you realize you have no idea what the last page said? Reading AI writing is always like that for me; if it’s something I’m going to be in any way responsible for, I consistently have to close the file and write my own, rather than try to edit it.

I’ll link you to some other folks’ reports of their experiences with it, as well:

• Bluesky thread that describes the problem (with, sorry, a good deal of profanity and nothing positive to say about genAI)
• Single Bluesky post about the difficulty of double-checking flawed output, when one already questions themselves (includes unkind words about AI boosters)
• Bsky post about the difficulty of reading LLM output for someone with ADHD, plus two more posts saying the same thing

AI outputs must be understood to be corrected (and they always need to be corrected)

• “Before we can safely change code, we first need to understand it – understand what it does, and also oftentimes why it does it the way it does. In that sense, this is nothing new.
  
  “What is new is the scale of the problem being created as lightning-speed code generators spew reams of unread code into millions of projects.
  
  “Teams that care about quality will take the time to review and understand (and more often than not, rework) LLM-generated code before it makes it into the repo. This slows things down, to the extent that any time saved using the LLM coding assistant is often canceled out by the downstream effort.” – Comprehension Debt: The Ticking Time Bomb of LLM-Generated Code, by Jason Gorman, September 2025.
• Companies are drowning in code, without enough people to read, understand, and review it. The big bang: a.i. has created a code overload, by Mike Isaac and Erin Griffith, April 2026.

Ethics and the environment

I pushed this part to the bottom, despite it being so much a part of my original decision to become an AI vegetarian, because every organization creating an AI policy (unless that policy can be summarized as “don’t”) will already have determined that these concerns are secondary to their other goals. Organizations might give lip service to “using AI ethically,” but the people whose opinions I most respect on this topic generally argue that, for people empowered to make organizational decisions, there is no ethical use of generative AI products. (Well, OK, a small subset of the people I most respect are arguing that, perhaps, if you use small models on standard consumer hardware, maybe it’s not so bad — again, they’re arguing from an ethical and environmental standpoint, and many of the harms outlined above still apply. I actually have a use for Whisper, though you’ll note I spent all this time writing this post, rather than deploying it on my Mac.)

At a minimum, I think that if you have the money, you should spend the $20 (unless you can find a free screening near you) and just under 2 hours to watch Ghost in the Machine. It does a pretty good job describing the eugenicist aspects of AI, and, crucially, it spends time with data workers in the Global South whose work makes these products possible. It’s less gut-wrenching than reading some of their first-hand accounts, but it’s enough that you can no longer pretend to think these products aren’t hurting anyone, just by existing. If you’re more interested in the growing US data underclass, Karen Hao just released a 16 minute documentary video, which is free on YouTube.

Some other folks who have done a better job covering the social and environmental costs than I ever could:

• Mulvaney, Katy. (2026). Don’t use AI. Southern Connecticut State University.
• Guest, O., Suarez, M., Müller, B., van Meerkerk, E., Oude Groote Beverborg, A., de Haan, R., Reyes Elizondo, A., Blokpoel, M., Scharfenberg, N., Kleinherenbrink, A., Camerino, I., Woensdregt, M., Monett, D., Brown, J., Avraamidou, L., Alenda-Demoutiez, J., Hermans, F., & van Rooij, I. (2025). Against the Uncritical Adoption of ‘AI’ Technologies in Academia. Zenodo.
• Slater, K. (2025). Against AI: Critical Refusal in the Library. Library Trends 73(4), 588-608. 

I want to shout out to people, books, and articles that helped organize and clarify my thinking, even if I didn’t cite them explicitly here

• (people) The AI Skeptics Reading Group, which you can join too if you’re interested.
• (article) Baer, Andrea. (2025). Investigating the “Feeling Rules” of Generative AI and Imagining Alternative Futures, published in In the Library with the Lead Pipe
• (book) Bender and Hanna. (2025). The AI Con.
• (book) Eubanks, Virginia. (2018). Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor. (yes, this is about traditional ML, but it’s still relevant)
• (book) McQuillan, Dan. (2022). Resisting AI: An Anti-fascist Approach to Artificial Intelligence.
• (article) Moser, Anthony. (2025). I am an AI hater.
• (book) Madhumita, Murgia. (2024). Code Dependent: Living in the Shadow of AI.
• (article) Potter, Ned. (2026). What do we know about GenAI?
• (speech, transcribed) Watters, Audrey. (2025). AI Grief Observed.

And why not share my TBR (to-be-read) list, while we’re here

• Williams, Rua. (2025). Disabling Intelligences: Legacies of Eugenics and How We are Wrong about AI.
• Hao, Karen. (2025). Empire of AI: Dreams and nightmares in Sam Altman’s OpenAI.
• Merchant, Brian. (2023). Blood in the Machine: The Origins of the Rebellion Against Big Tech.
• Benjamin, Ruha. (2019). Race After Technology: Abolitionist Tools for the New Jim Code.

A final note:

I’m ready to hit “publish,” but at the same time, this does not quite feel finished. I reserve the right to come back and add citations (and you should feel comfortable sending me any big ones I missed, of course!) and, if needed, entire sections. I apologize if that messes up your RSS reader or is otherwise frustrating for you.

In the wake of copy.fail, there are more vulnerabilities that have been announced:

• Copy Fail 2: Electric Boogaloo
• Dirty Frag

Right now would be one of the best times for a supply chain attack via NPM to hit hard.

Outside of Linux kernel patches from your distro, I think it's probably a good idea to put a moratorium on installing new software for a week or so.

The release candidate for Evergreen 3.17 is now available. As compared to the third beta of 3.17, it contains some installation and packaging fixes as well as a minor cleanup of unused files. As noted with the third beta release, Evergreen 3.17 includes a significant upgrade of the Angular framework used by the staff interface. Some minor user interface issues have been identified that will be resolved for 3.17.0 or 3.17.1, but additional testing is strongly encouraged for all staff interfaces.

The release candidate is available for download. General release is scheduled for May 13, 2026.

What testing commercial neurotech has taught us, and why these technologies require informed engagement now.

The post The Neurotech Booth at RightsCon That Never Happened first appeared on Open Knowledge Blog.

We are grateful to our colleague and guest author Omar Farhoud, Sales Manager in the EMEA region, for sharing his perspective on how AI might benefit Arabic-language metadata workflows, as well as risks and limitations to be aware of. For more on implementing AI in metadata workflows, please see our blog series on the topic.

Photo by Nahrizul Kadri on Unsplash

Recent discussions on the OCLC Research blog have explored how artificial intelligence (AI) is beginning to reshape cataloging and metadata workflows, particularly in addressing backlogs and improving efficiency. Conversations like these, however, are often grounded in environments where English-language metadata dominates. Bringing in a Middle Eastern perspective, especially from Arabic-speaking libraries, introduces a different set of conditions shaped by multilingual practice, script diversity, and issues of representation.

Arabic metadata workflows are not edge cases. They represent everyday operational realities across academic, national, and public libraries in the region. As such, they provide a valuable lens through which to examine both the opportunities and the limits of AI in library systems, including the interplay between automated workflows and human oversight.

Arabic metadata as a multilingual workflow

Cataloging in many Middle Eastern libraries is inherently multilingual. Records are typically created in Arabic and English, and in some cases French. This creates a dual responsibility: maintaining consistency within each language while ensuring coherence across scripts.

Within OCLC cataloging environments such as Connexion client, this multilingualism is embedded in the bibliographic record structure. Arabic script fields are paired with romanized equivalents, following established transliteration standards such as ALA-LC. A single intellectual work may therefore exist in parallel representations that must remain aligned over time.

There are also technical considerations. Cataloging in Arabic depends on appropriate input methods, keyboard configurations, and support for non-Latin scripts within MARC environments. These infrastructural elements directly affect both efficiency and data quality.

Transliteration sits at the core of this workflow. While systems provide automated support, outputs frequently require manual correction. Arabic is highly context-sensitive, and small variations in spelling can significantly alter meaning.

Transliteration remains a strong candidate for improvement. Current approaches are largely rule-based. AI models, especially those trained on high-quality bilingual corpora, could offer more context-sensitive transliteration suggestions. However, these would still require validation, reinforcing the need for human oversight.

Discovery expectations and normalization practices

On the discovery side, user expectations introduce an additional layer of complexity. Arabic users expect search systems to handle orthographic variation seamlessly, without requiring precise input.

Recent enhancements in WorldCat Discovery illustrate how this is achieved through rule-based normalization. These include treating diacritics and non-diacritics as equivalent, normalizing character variants (such as different forms of alef), handling prefixes like “ال”, and ignoring elongation characters. Sorting rules are also adapted to reflect Arabic linguistic conventions.

What appears as simple search functionality is underpinned by carefully designed and tested normalization rules. These are deterministic and transparent, refined over time based on real usage patterns.

This is an important benchmark for AI. Any AI-driven approach to metadata creation or discovery must match or exceed this level of linguistic precision. Moreover, AI could extend normalization into cataloging workflows. While discovery systems normalize at query time, catalog records themselves often retain inconsistencies, particularly in legacy data. Machine learning models could assist in identifying and aligning variant forms across large datasets.

From local workflows to shared discovery infrastructures

A broader shift is also taking place in how Arabic collections are positioned within global discovery systems. Aggregated discovery initiatives, such as shared Arabic-language catalogs built on WorldCat infrastructure, reflect a move away from isolated local systems toward more integrated and visible ecosystems.

Field discussions with libraries across the Middle East point to a consistent concern: the global visibility of Arabic scholarship. Fragmentation in discovery and inconsistencies in metadata continue to limit access and representation.

From a strategic perspective, this aligns with broader discussions on the “collective collection” and the role of shared infrastructure in improving resource discovery. AI, when combined with such infrastructure, could help improve metadata consistency at scale and support cross-institutional alignment.

Risks and limitations

Despite these opportunities, the use of AI in Arabic metadata workflows raises several important concerns:

• Language bias remains a significant issue. Many AI models are trained predominantly on English-language data, leading to uneven performance in Arabic. This reflects broader critiques of AI systems as reproducing existing linguistic and cultural imbalances.
• Transliteration introduces additional risks. While rule-based systems are predictable, AI-driven approaches may produce variable outputs that are harder to standardize. This variability can undermine authority control and consistency.
• There is also the risk of losing semantic nuance. Arabic names and terms often carry cultural and contextual meanings that may not be captured by automated systems.
• Normalization itself must be approached carefully. Rule-based normalization is controlled and transparent. AI systems, by contrast, may over-normalize, removing distinctions that are meaningful within the data.

Shared implications for AI implementations and professional practice

Arabic metadata workflows reinforce several broader insights that emerged from OCLC Research’s earlier examination of AI and metadata management.

First, hybrid models are likely to be the most effective. AI can improve efficiency and scalability, but it does not replace the need for professional expertise. Human validation remains essential, particularly in linguistically complex environments. The earlier OCLC Research findings corroborate this, noting “the importance of designing AI implementations as enhancements to human expertise rather than replacements, ensuring that professional development pathways remain robust while leveraging AI’s potential to handle volume and routine tasks.”

Second, there is a need to move beyond English-centric assumptions in system design. Supporting multilingual knowledge infrastructures requires deeper engagement with linguistic diversity at the level of data, standards, and workflows. Again, this observation from Arabic metadata workflows finds a parallel with our earlier findings, which emphasize that “AI systems often lack the deep contextual understanding needed for community-specific terminology or cultural nuances that don’t appear in general training databases.”

Third, metadata enrichment is an area of growing interest. Many Arabic collections lack detailed subject metadata. AI could support the generation of subject headings, summaries, and keywords in Arabic. Our earlier findings also noted the opportunity AI affords for metadata enrichment: for example, institutional repository deposit processes “often fail to supply complete and accurate metadata because students and researchers find metadata creation burdensome and time-consuming.” AI-powered support in areas like subject heading suggestion or automated abstract generation can help close that gap.

Fourth, AI could contribute to backlog reduction by generating draft records or recommendations. This use case was also highlighted in OCLC Research’s earlier findings on AI and metadata workflows: “AI-generated brief records for these materials can enable them to appear in discovery systems earlier, accelerating the process of making hidden collections discoverable and supporting local inventory control. This approach addresses the immediate need for discovery while allowing records to be completed, enriched, or refined over time.”

Arabic metadata workflows present unique features that differ from English language-based systems, which in turn impact specific use cases for AI implementations. Yet as the preceding examples illustrate, there is also general perspective regarding AI-powered metadata workflows that applies equally to Arabic and non-Arabic systems alike. Perhaps most important is the observation that in considering AI implementations, the goal is augmentation rather than replacement, supporting catalogers in focusing their expertise where it adds the most value. There is a tendency in current debates to frame AI adoption as a binary choice between automation and professional control. But this framing is limiting. AI is more usefully understood as part of a continuum of human–machine collaboration, where the question is not just whether to use AI, but how, where, and under what constraints.

Looking ahead

A pragmatic approach is emerging across libraries in the Middle East. Institutions are exploring targeted AI applications, particularly in normalization, enrichment, and transliteration, while maintaining strong human oversight.

There is also an opportunity for collective action. Improving Arabic-language training datasets, strengthening authority control frameworks, and promoting collaboration across institutions will be critical for making AI effective in this space.

Developments in systems such as Connexion and WorldCat Discovery show that progress is already underway. AI can accelerate this work, but only if it is grounded in real workflows and informed by linguistic and cultural expertise.

Ultimately, this is not only a question of efficiency. It is a question of representation. Ensuring that Arabic knowledge is accurately described and fully visible within global discovery systems remains a central challenge and a meaningful test of how inclusive our infrastructures truly are.

The post No edge case: Understanding AI opportunities through Arabic metadata workflows appeared first on Hanging Together.

LibraryThing is pleased to sit down this month with author and romance enthusiast Katie Holt, whose 2024 debut, Not In My Book, follows the stormy relationship between a romance novelist and a literary author who are forced to work together on a book. A New York City resident and Tennessee native, Holt earned her degree in English and Creative Writing at NYU, while working at famed Manhattan bookstore, The Strand. When not writing, she works at St. Martin’s Press as an assistant editor. Her second novel, The Last Page, about the romance between a bookseller who dreams of running the bookstore where she works and the owner’s oblivious grandson who has inherited everything, is due out from Alcove Press later in May. Holt sat down with Abigail this month to discuss her new book.

How did the idea for The Last Page first come to you? Were you inspired by your own time as a bookseller? (Full disclosure: I also worked at The Strand for a number of years). Were other bookstore romances, like the one in You’ve Got Mail, an influence?

A fellow Strand-er!! That’s so cool! We’ll definitely have to see if we crossed paths with any of the same booksellers.

I was definitely inspired by my time there. I think it’d be impossible not to be! The Strand has a rich history and while I worked there, lots of the booksellers told me stories about Ben McFall. They had nothing but wonderful things to say about them and it was obvious he left such a deep mark on The Strand. I thought a lot about what it meant to leave behind a legacy in a bookstore. To be surrounded by books and for him to really be the most talked. I never met him, of course, so Leo was really inspired by my late grandfather. He was the coolest person I’ve ever met in my entire life and was a huge reader and supporter of literature.

I’m sure you know that the group of booksellers at The Strand were opinionated and eclectic and I loved it. I’m still friends with so many booksellers there and adore them. The bookseller relationships and dynamics were inspired by them for sure.

Nora Ephron is one of my greatest inspirations, so totally! You’ve Got Mail also has such a fun crew that are all different from each other, but they come together to create some hilarious scenes. I’ve always loved Notting Hill, too, and how that small shop just seems to be bursting with books. The Last Page has plenty of room in their store, but in my mind, books are just spilling over every edge of every surface.

You’ve been a romance advocate in both your professional and academic life, arguing to your college professors that the genre was worthy of consideration. What makes romance so special to you, and why should readers pay attention to it?

Oh goodness, I could wax poetic for days. I always say my favorite part of the romance genre is the HEA (happily ever after). That readers are guaranteed a safety net and it allows writers to explore some really heavy topics! The Last Page discusses grief throughout—what it means to different people, how it can ebb and flow, and how it doesn’t always make sense. Henry is also someone who suffers from depression and feels really embarrassed about it. I loved that I could discuss these topics the way I wanted to and tell the reader, “Don’t worry. They’re safe and you are, too.”

A lot of the time in college, I was told that these character arcs or plots I wrote were devalued by interweaving a love story that ends happily. I had one professor who always said, “I don’t buy it. I don’t buy that all the characters you write end up together.” Well, they do! It always blew my mind. Why would love decrease the value of anything? Isn’t it beautiful that you can evolve and change and grow and fall in love with someone who’s watched all of that happen? Who loves you before, after, and throughout?

There’s also something really special about how the romance genre varies so much within the genre. To me, it demonstrates how everyone views love differently and how everyone loves differently. I just think it’s beautiful and something special and worthy of celebrating.

The Last Page features a bookseller from New York City and a bookstore owner from Tennessee—a profession and two places that have been important to you personally. Are there specific spots or incidents in Ella and Henry’s tale that were inspired by your own life story?

I’m definitely inspired by my life in the city, and it’s impossible for me to not write about the things happening around me. (However, I unfortunately didn’t fall in love with a hot, shy, nerdy former football player while working at The Strand). New York is such a romantic city and I work hard to make the city feel like a character in the book. A reader
tagged me in a NOT IN MY BOOK book tour they did across NYC when they were visiting and I burst into tears. Those pictures of them at Peculiar Pub and Washington Square Park were exactly why I include real restaurants and locations. Specifically in this book, Ella and Henry go to Kingston Hall, which is one of my favorite places. It’s buy one get one free beer on the weekends!! And there’s a great pool table and such interesting interior design. I’ve brought my laptop there plenty of times to get some writing done and thought it was such a cool place for them to stumble into.

Although I loved being a bookseller, I did have the quintessential yet frustrating “I saw a book with a black cover here three weeks ago. Can you help me find it?” which I include in The Last Page. Bill Clinton also did come in and sift through American History. There wasn’t a naked man that ran through the store—he had on a speedo in real life.

Something that was really inspired by my life was Leo. Like I said, he was based on my grandfather who I was really close to. He had been pretty sick for a few years and every time I saw him, I braced myself because it could’ve been the last. The thought of losing him haunted me and I think I wrote this in a place of nearly preemptive grief? I kind of think of it like a letter from my past self telling me that I’ll survive losing him, even if it hurts like hell sometimes. He never got to hold a copy of The Last Page, though, I know he would’ve been proud and read every single word (as he mortifyingly told me he did with Not In My Book).

Tell us a little bit about your writing process—how and where you like to write, and how you construct your stories. Are you a plotter or a pantser?

I have to plot. I recently finished writing a book that I tried to pants and it was horrible. I hated every single second of it, deleted everything, and started over.

My process has evolved as my life has! For Not In My Book, I was home for the pandemic and wrote the first draft in my childhood bed and my parent’s kitchen table. I had to have my Beats on and a bag of BBQ chips and Kombucha by my side (in my head they counteracted each other??).

Nowadays, though, I love to write in cafes or bars in the city. I’ve always thought it was very Cool Girl when I saw people writing or reading at bars. Recently, I’ve been grabbing my notebook and physically writing out scenes or chapters. I also love to listen to music while I write (mostly Taylor Swift) and think about how the characters fit that song or if they ever have. Because Rosie definitely listened to “Welcome to New York” for a month straight when she moved to New York and Ella definitely had “Honey” on a constant loop.

What advice would you give to up-and-coming authors, particularly up-and-coming romance authors?

When I was in high school, I took a writing course with Rachel Carter and it completely transformed the way I wrote and my perspective on it. She told me to write as much as I read and read a lot. The only way to know the mechanics of the genre or even a book is to completely entrench yourself in it. Read lots and lots of books and write every day. Even if it’s just a sentence! Writing is a muscle and if you don’t exercise it, it’ll weaken.

I also think it’s important to give yourself grace. No one is publishing their very first draft. It’s okay for your writing to be terrible and make sense to no one else but you. Sometimes it’s all in the revisions! But don’t be too hard on yourself.

What’s next for you? Do you have any books currently in the works?

I have a couple of things in the works!! I’m working on two books right now that are very different from each other. I don’t want to reveal too much just yet, but these really feel like the stories I’ve been waiting to tell for sometime, so I’m super excited about them!

Tell us about your library. What’s on your own shelves?

All kinds of romance! I really don’t shy away from any of the subgenres. I’ve been into speculative and dark recently, but I’m hoping historical makes a resurgence, especially now that Lisa Kleypas is back!

It’s really rare I read outside of romance, but that’s my small goal for this year. I’ve always been a physical reader, but I’m dabbling in some book club fiction and nonfiction through my audiobooks.

What have you been reading lately, and what would you recommend to other readers?

I just finished Tessa Bailey’s new time travel romance, Broken Rival, and I feel like I’m having a parasocial relationship with them. Like…it’s that serious. A Little Buzzed by Alys Murray was so intensely sexy that my Apple Watch asked if I was working out because my heartbeat was so high. That is a MUST read! I also read The Heartbreak Hotel by Ellen O’Clover recently and was stunned by it and the musings on love and life after a breakup. The characters are so well drawn and she strikes the exact right balance of emotionally intense and absolute yearning. Secret Nights and Northern Lights by Megan Oliver is also what second chance romance dreams are made of. I cannot wait to read more from her.

Potential Attack Target

White Hats

Catch-22

Black Hats

Conclusion

Together, we are developing a tool that will bring public data closer to citizens, allowing them to get data by simply asking a question.

The post Brazil and OKFN partner to enable citizens to ‘talk’ to national data first appeared on Open Knowledge Blog.

OpenRefine has an API. Not just for reconciliation, but an API you can use to perform actions from creating/deleting a project to blanking down columns and mass editing. Even some of the more experienced users I’ve asked were surprised by this.

I think if you’d asked me before this month, I’d have said “Maybe?” I’ve spent enough time in the documentation to see it mentioned, but hadn’t spent any time investigating.

Like many OpenRefine users, much of what I do involves looking at the data, considering what I need to do next, performing an action or two, evaluating the outcome, repeat. That doesn’t lend itself well to API work. But sometimes, as I’ve mentioned in the previous blog posts, I’ve been doing a lot of repeating tasks at the start of projects. Once those are performed, I need to visit the project sheet and assess the data. So when I was first trying to figure out how to repeat operations, I decided to learn more about the API.

API Basics

Because OpenRefine runs on a local server (or you could host an instance on an actual server), you can send a pretty straightforward set of GET or POST actions at that URL. You can even create projects using the API, with appropriate parameters, though I haven’t yet teased that out. Data has to be sent as multipart/form-data.

While I’m going to be using the “perform operations” function, which is documented, I noticed a lot of actions in the CSRF writeup that were missing in the official API documentation. When I have some free time – if I have some free time – I may try these out.

Authentication

Even if you’re running it locally, you’ll need to get a CSRF token for POST requests which change the data in anyway.

To handle this in my fuller Python script, I wrote/repurposed the following code:

def auth_me():
  '''auth function broken out'''
  response = requests.get(server + "/command/core/get-csrf-token", params={"project":project_id})
  access = response.json()["token"]
  return access

Performing Operations With the API

Performing operations is a simple POST. This is my very simple Python, which only requires requests and json libraries (there are actual OpenRefine clients, but the Python client’s GitHub page was archived by its owner so I didn’t want to rely on it). The code is commented, but essentially I:

1. Set the server
2. Set the project ID
3. Define and perform the auth
4. Paste in the operations copied as described in the previous post and dumps it into JSON. Critically, however, I had to change false to False or Python got mad. I don’t know if it would be better to put “false” in quotes, that’s another thing to test more (maybe using a case which should be True, or where false is more noticeable). I didn’t need to repeat and this works.
5. Using requests, post to perform-operations url. Pass on parameters of the project ID, the csrf key, and the actual operations I want to perform. For the sake of code lenght, this is a much shorter set of operations than I was actually performing.
6. Process the result and print something which helps me understand if it was successful or not.

import requests, json

## set variable on the off-chance it's a different server sometime
server = "http://127.0.0.1:3333"

## could be set as an argument. could also be redone as a list of IDs and iterate through them to perform the same functions (adjustments needed below)
project_id = "18117329352"

## gets new auth code each time, I don't know when they expire
def auth_me():
  '''auth function broken out'''
  response = requests.get(server + "/command/core/get-csrf-token", params={"project":project_id})
  access = response.json()["token"]
  return access

## now do actual auth
key = auth_me()

## simple pasted in entire output and changed "false" to "False" and then threw into a json.dumps
post_operations=json.dumps([
  {
    "op": "core/blank-down",
    "engineConfig": {
      "facets": [],
      "mode": "row-based"
    },
    "columnName": "Catalog Key",
    "description": "Blank down cells in column Catalog Key"
  },
  {
    "op": "core/text-transform",
    "engineConfig": {
      "facets": [],
      "mode": "row-based"
    },
    "columnName": "Title",
    "expression": "grel:value + \" - \" + row.record.index",
    "onError": "keep-original",
    "repeat": False,
    "repeatCount": 10,
    "description": "Text transform on cells in column Title using expression grel:value + \" - \" + row.record.index"
  }
])

## now perform the operation
perform_operations = requests.post(server + "/command/core/apply-operations",
  params=
    {
      "project":project_id,
      "operations":post_operations,
      "csrf_token":key
      })

## oh my god tell me what happened
if perform_operations.status_code == 200:
  if perform_operations.json()["code"] == "ok":
    print("Operations performed successfully")
  else:
    print(json.loads(perform_operations.json()))
else:
  print("Status code:",perform_operations.status_code)

Evaluating Efficiency

As a one-off, I don’t think using the API for this purpose is going to be more efficient than simply downloading a copy of the operations I want to replicate and uploading the file to each new project.

What would tip it over into efficiency for me would be if I could figure out how to create projects, do those as a batch, get the IDs, and then run a second step to perform all the operations on them as a list.

As I said at the beginning, I think a major reason everything from repeating functions to how to use the API isn’t more widely-known among regular practitioners like myself is that we’re so rarely doing something with this kind of repeated process. I don’t think I’ll find the API helpful for most things I do. But when I’m on a big project like this one? I’ll keep exploring.