I missed this fascinating 2022 post on the history of PostScript (and, by extension, the PDF)—by David Brock for the Computer History Museum:

From the start of Adobe Systems Incorporated (now Adobe, Inc.) exactly forty years ago in December 1982, the firm’s cofounders envisioned a new kind of printing press—one that was fundamentally digital, using the latest advances in computing. Initial discussions by cofounders Chuck Geschke and John Warnock with computer-makers such as Digital Equipment Corporation and Apple convinced them that software was the key to the new digital printing press. Their vision: Any computer could connect with printers and typesetters via a common language to print words and images at the highest fidelity. Led by Warnock, Adobe assembled a team of skillful and creative programmers to create this new language. In addition to the two cofounders, the team included Doug Brotz, Bill Paxton, and Ed Taft. The language they created was in fact a complete programming language, named PostScript, and was released by Adobe in 1984.

The source code from 1984 is included.

I’ve had some interesting comments on the “Matthew Effect in AI Summary” piece I published last month at LSE Impact, and thought to share a couple. One was posted by Nicola Willand:

It absolutely happened to me! In 2023 I (female) took the doi of my most cited paper, published during my PhD, and put it into Research Rabbit for a quick bibliometric analysis. In the results it put a (male) Prof of my university as the first author. He was not even in my supervisory team and definitely had nothing to do with the paper, but I had published with him (with me as first author) post-PhD. I was named as the second author. I then thought if I export it into Endnote, Endnote will correct its mistake. It did not. The Prof was still first author. If anyone then cited this incorrectly detailed paper, the Prof’s citations would increase undeservedly and unfairly. I was shocked and unreasonably angry with the Prof 🙂 I repeated the exercise in 2024 with the same incorrect attribution. I have been too afraid to repeat the exercise since but I warn my students about this and use it to demonstrate bias. Thank you for giving it a name 🙂

Another commenter, Aaron Tay, politely challenged the notion that Research Rabbit in 2023 [and 2024] is AI, in the LLM sense. Tay offered a lengthy comment on my piece, pushing back thoughtfully (my replies in brackets):

I think while it is hard to deny LLMs encode streotypes and biases from training data, I think whether AI search tools e.g RAG enabled, Deep Research lead to a Matthew effect (or worse effect) compared to current tools in use is a empirical matter. [agreed—JP]

At end of the day, the question is about retrevial (if you can’t retrieve, you can’t generate/summarise). Because academic search engines (AI or not) are complicated mix of technology and techniques and the degree in which LLMs impact the retrieval differs greatly depending on the tool, you cannot jump to the conclusion they will definitely lead to the Mathhew effect or any other bias exhibited by just the LLM alone. [also agreed—JP]

For example, there are at least 2 studies that have studied this directly and they actually conclude the AI search tools in their study had the opposite effect and led to citing highly cited papers LESS. (see https://allenai.org/blog/asta-citations and https://www.science.org/doi/full/10.1126/science.adw3000). [I see only one study, the Science one, but Tay summarizes it fairly—JP]

As someone who has some experience testing and using such AI search tools, I can tell you the fact that these tools and search deeply and surface papers that are spot on relevant but lesser known and would have been missed if you used Google Scholar or a conventional database. So this could be part of the explaination… [fair—JP]

Finally, I would like to make the point that for many AI search engines, LLMs plays a relatively minor role in retrieval. For example, I was recently looking at results for a study for a different matter and I noticed the tool Consensus.app would randomly have totally irrelevant papers ranked high. Looking at the offenders (extreme high citation outliers) and a look at the documentation (Consensus’s 2nd stage ranking is described as weighting quality, defined as high citation, recency and quality journal) explained what was happening. [very interesting, and could be true of the big-AI and publisher Deep Researches, but they haven’t said]

Notice this analysis has nothing to do with LLMs. In fact, each of the “AI search engines” I studied had very different characteristics, and I am highly skeptical you can tarr all of them with one brush e.g. they all lead to bias X. [agreed again—JP]

I really appreciate both comments, including Tay’s. I have probably come across his name, but wasn’t aware of his blog, Aaron Tay’s Musings about Librarianship. It’s smart and informed—consider his latest post, which uses early Google Scholar complaints measured against its current usefulness to urge that we all keep an open mind about academic AI search tools. I also recommend this summary post from a week earlier. He catalogs his year of testing of the tools, an impressive array indeed.

Tay is coming from a pragmatic perspective—are these tools good for academic users, librarians included? He’s a booster, in that respect, with some justified expectation that the tools will get better. While I agree with his tar-all-with-one-brush worry, the issues I’m addressing in the Matthew effect piece aren’t about usefulness—they’re about the credit system and the potential for patterned injustice. What does he make of the four studies I cited (here, here, here, and here) that find early evidence for a Matthew effect? I’ll write to ask :).

I missed this short Sam Moore piece from the fall:

Even though open access was motivated partly by the desire to rein in commercial publishers’ profiteering, it has in many ways simply reinforced their dominance.

The article is a taut overview of the good work Sam and colleagues like Janneke Adema have been doing to promote “scaling small” and community-led publishing. As Sam also argues in his important Publishing Beyond the Market, OA as an end in itself has become, perhaps, a distraction:

The open access movement has been successful at making papers freely available, but perhaps it has now run its course as a useful way of thinking about the future of publishing. Should we re-orient our efforts towards the scholarly commons and the promise it holds for non-commercial, community-led publishing? I think so.

Jonny Saunders (he of the Institute of Pirate Technology), continues to amaze:

The world is Big Data, and The Cloud is its landlord. It is our responsibility to ferret it out of its primitive unknown, mine it, harvest it, dump it by the tanker-truckful into great Data Lakes overhung by computational Clouds to refine the Actionable Insights from its desiccated husk. The Cloud promises us an infinite, seamless expanse of Knowledge. If only we can harness the wily spray of our Organic Content, filtering our every action, affection, and affiliation through a thicket of algorithmically optimized platforms then The Cloud might teach us enough about ourselves to finally be happy.

These are the opening sentences of his brilliant paper, last updated in 2015, on “surveillance graphs” and corporate capture. The piece is ambitious, layering itself above scholarly communication, above the “cloud,” above LLMs, to focus on the “Knowledge Graph of Everything”—enabled by naive publicly funded infrastructure projects. His solution? “Vulgar linked data”:

Predicated on relationality, heterogeneity, distribution of power, and vernacular expression, vulgar linked data infrastructures attempt to empower people to socially organize information in a truly decentralized sociotechnological commons, rather than empowering systems to rent knowledge organization for profit.

This piece deserves a hell of a lot more (human) attention.

Invest in Open Infrastructure’s Lauren Collister, in a January post based on an interview with Knowledge Commons’ Kathleen Fitzpatrick:

The core of that difference is that Knowledge Commons is committed to remaining free and open, to never selling user data, to never pursuing for-profit operation. But sustainability requires support from the community it serves. "We are committed to remaining not-for-profit," Fitzpatrick says. "But in order to do that, we really, really need help." That help comes from the community of institutions and individuals who invest in the Commons and participate in governance to chart the future of their shared endeavor. "As an institution joins the network, they get a seat at the table," Fitzpatrick emphasizes. "Their ability to help shape the future of the platform is its sustainability model, its governance model. That is how we transform from a platform that’s free to use right now into a genuine commons."

This chicken-and-egg problem, and the “vendor mode” thinking that Fitzpatrick details as a barrier in getting library buy-in, is a huge challenge—even among seemingly sympathetic librarians. It’s a dead horse beating, but I remain convinced that a mission-aligned funding exchange centered on nonprofit scholarly pub infrastructure is desperately needed. Invest in Open Infrastructure?

Just published in LSE Impact, on the “deep research” summary-and-citation tools from both the AI giants and the likes of Elsevier:

At the core of all these tools is a series of verbs: to surface, to rank, to summarise, and to recommend. In this sense the AI products are already acting as knowledge arbitrators, by picking winners and losers according to what they make visible. There are two big and interlocking problems with this role: the models are trained on the scholarly past, and their filtering logic is inscrutable. As a result, they may smuggle in the many biases that mark the history of scholarship, around gender, geography, and other lines of difference.

In this context it is useful to revive an old concept in the sociology of science. According to the Matthew effect, named by Robert Merton decades ago, prominent and well-cited scholars tend to receive still more prominence and citations. The flip side is that less-cited scholars tend to slip into further obscurity over time. These dynamics of cumulative advantage have, in practice, served to amplify the knowledge system’s patterned inequalities, with, for example, diminished citations for female scientists, a development aptly labeled the Matilda effect by Margaret Rossiter in 1993.

The deployment of generative AI tools in scholarship, as their use picks up, may produce an even worse Matthew effect, on the scale of Elsevier and with no paper trail. The problem is analogous to the well-documented bias-smuggling with existing generative models; image tools trained on, for example, mostly white and male photos reproduce the skew in their prompt-generated outputs. With our bias-laden scholarship as training data (“120 years of research”, in Web of Science’s boast), the research models may spit out results that, in effect, double-down on inequality.

What is worse is that we won’t really know, due to the models’ black-boxed character. Thus, the tools may act as laundering machines—context-erasing abstractions that disguise their probabilistic “reasoning”. Existing biases, like male academics’ propensity for self-citation, may win a fresh coat of algorithmic legitimacy. Or consider centre-periphery dynamics along North-South and native-English-speaking lines: Gaps once traceable to geopolitical history, including the legacy of European colonialism, may be buried still deeper. The models, in short, could serve as privilege multipliers. Early evidence suggests that’s exactly what’s happening.

The piece is here.