The information conglomerate Thomson Reuters, in a press release announcing an “expanded vision” for its “professional-grade GenAI assistant”:

CoCounsel is an AI assistant that acts like a team member – handling complex tasks with natural language understanding. Completing tasks at superhuman speeds, CoCounsel provides high-quality information at the right time, maintains multiple threads of work, as well as keeping context and memory across the different tasks and products customers use each day. By augmenting professional work with GenAI skills, CoCounsel delivers accelerated and streamlined workflows, enables professionals to produce higher-quality work more quickly, all while keeping customer data secure.¹

The CoCounsel name is, it seems, a nod to Thomson Reuters’ Westview and other legal businesses—and a lazy riff on Microsoft’s Copilot. Either way, another publishing-adjacent colossus picking up its pace in the race to re-monetize “content” through AI.

My short piece in the Chronicle Review (paywalled, alas, but here’s a PDF), on C.P. Snow’s The Masters (1951):

What Snow captures is the outsize role pride plays in faculty life. We are, nearly all of us, vulnerable like this — a single snub is enough. We live in a hothouse of peer esteem, poised for humiliation, our dignity always in question. Snow shows this — or, rather, he tells it, through paragraphs of psychological portraiture. It’s this tell-not-show realism that struck Leavis as ponderous and cringeworthy. But Leavis is wrong: What’s best about The Masters is its sharply observed phenomenology. This is how the book transcends the cloistered male world of an unnamed Cambridge college in the late 1930s — why it feels fresh, even contemporary.

The piece—whose headline should be “Academic Life is About Injured Pride”—is really a footnote off of Vivian Gornick’s brilliant 2021 Harper’s essay “‘Put on the Diamonds’:Notes on Humiliation.”

STM—the Dutch-based trade group and self-proclaimed “standard bearer for the academic publishing industry”—joined over a dozen media-related associations to applaud passage of the EU’s Artificial Intelligence Act. The reason, of course, is the promise of AI training revenue from big U.S. tech firms:

[The Act] provides first tools for rightsholders to enforce their rights, including the obligations on providers of General Purpose AI (GPAI) to make available a sufficiently detailed summary of the works used for training their models, to retain detailed technical documentation and to demonstrate they have put in place policies to comply with EU copyright law, regardless of where they acquired data or trained and developed their AI models.

The scope of enforcement—together with the outcome of the US Copyright Office’s review and various court rulings—will determine how and whether the big five academic publisher-oligopolists squeezes still-more profit from our unpaid scholarship.

Jaap Nieuwenhuis, in a fun piece in The Information Society on paper titles:

The question “Should I Stay or Should I Go?” has been asked repeatedly in academic work since 1992, suggesting that it is still unclear whether people (or animals, or cells) should stay or go. If we consider science to be a cumulative process, in which researchers base their work on the findings from work done by others before them, we can conclude that in this specific field of inquiry, we have yet to reach a scientific consensus. Moreover, considering the incremental rate at which “Stay or Go” contributions are being published, it is likely that this consensus is still far in the future.

The 1982 Clash song has graced 408 paper titles, according to Nieuwenhuis.

A suggestion for future research is no longer use “Should I Stay or Should I Go?” in the titles of research articles. Building upon this suggestion, another may be to first do a literature search to check if the title you came up with has already been used. Like in music, a cover song can be refreshing now and then, and sometimes even be better than the original; however, after 408 times, this may not happen anymore.

Times Higher Education, on UK ‘university-business collaborations’ [paywall]:

Figures from the Higher Education Statistics Agency (Hesa) show that 76,619 university-business interactions occurred in 2022-23. This was down by 5 per cent from the number registered in 2021-22, and the lowest level since at least 2014-15 – including years that were affected by the Covid-19 pandemic.

The story is pegged, ironically, to financial pressures—that a cash crunch is hobbling university-business “collaboration.” But that’s not what’s striking: The main takeaway, instead, is that entanglements with corporations are so normalized (in the UK, yes, but most everywhere) that there’s a countable “interactions” measure—with 76,619 registered as a disappointment.

Business-university ties go back centuries, but it’s easy to forget that before the late 1970s, such partnerships were rare.

Elsevier’s Judy Verses, in a February interview with The Scholarly Kitchen:

At Elsevier we try to support researchers, librarians, academic leaders, funders and governments by combining quality information and data sets with analytical tools to facilitate insights and critical decision-making. Science is increasingly more multidisciplinary, reproducible and transparent. The volume of research continues to grow, and researchers are increasingly sharing their datasets. These trends mean we have a clear opportunity to help the research community with our journals, our platforms, analytics capabilities, the use of artificial intelligence and machine learning to surface what is relevant, make connections and make smart decisions.

The whole interview is press-release pablum, but this paragraph is fascinating. Verses, Elsevier’s president of “Academic and Government Markets,” is trading in a revealing self-contradiction: She’s referring, on the one hand, to Elsevier’s propriety analytics tools, but also—with conflationary jujitsu—dataset-sharing in (open) science. The RELX Group’s data-hoovering is, to the state the obvious, anything but “transparent,” and by “sharing” they mean selling our surveilled behavior back to us—as high-priced prediction products.