Forbes:

OpenAI—the artificial intelligence company behind the viral ChatGPT chatbot program—is in discussions to sell shares valuing the firm at $29 billion, according to the Wall Street Journal, after the launch of ChatGPT was lauded by many as a revolutionary advance in artificial intelligence despite some problems.

Remember when OpenAI was nonprofit?

Librarian Aaron Tay, writing on Medium about the potential benefits of OA for machine learning–based projects:

one other benefit that tends to be overlooked, or at least seldom mentioned in my experience particularly by librarians, is how in an Open Access World, we can use machines to plough through the world’s research literature to look for patterns and even possibly do a synthesis of knowledge, leading to vastly greater effectiveness and efficiency in the way we do research…

It’s a smart, well-informed piece, with a nice gloss on the current status of models fed by academic papers. But “vastly greater effectiveness and efficiency”? Careful what we wish for. Among other things, models trained on existing literature risk generating a new Matthew Effect—a faster dynamic of cumulative advantage for the already visible and well-heeled.

Tay identifies emerging projects that attempt to, for example, aid literature searching and paper summation. He mentions Elicit, SciSpace, and Consensus, all three leveraging OpenAI’s GPT3. They’re fun to play with, but Tay doesn’t say that two of the three (SciSpace and Consensus) are for-profit startups. But that matters a lot: The companies are after profits, not knowledge, and they’re sitting ducks for acquisition by Elsevier et al.

Marcel Hobma, writing in the Journal of Trial and Error on the APC scourge:

There is evidence that article processing costs give older, more resourceful male researchers and prestigious institutions an advantage over authors from developing countries and early-career authors that are not backed by strong institutions […] this process can amplify certain publication biases that favour topics and viewpoints that are backed by rich organisations and industries, and therefore distort certain fields of research and possibly steer scientific research away from public interests.

The piece is the best overview of the mounting evidence on the APC regime’s exclusions and distortions.

Steven Mintz, writing for Inside Higher Ed on selfish colleagues in the humanities:

[I]f I were to point to a single factor that is most consequential, I’d draw attention to a dramatic shift in humanists’ professional identity. For better and worse, many and perhaps most humanities scholars, from the 1960s onward, identified first and foremost with their discipline, not with their institution or their department, let alone their students. […] I fear that we are witnessing the rise of a more extreme individualistic “out for themselves” ethic among humanities scholars. In my own department’s building, the hallways are empty except for a handful of students, office doors are closed and locked, and almost all their lights are out. Colleagues teach their classes, then depart to destinations unknown.

The piece—with passing nods to other factors like workload—blames faculty narcissism for what is a structural problem. The swinging 1960s aren’t the reason faculty won’t review manuscripts. It’s profiteering publishers, admin bean-counters, careerist students, and metricized review—academic capitalism—that have stamped out the collective project Mintz mourns.

From the statement:

The crucial role of software in research is becoming increasingly apparent, as is the urgent need to sustain it and to invest in the people who develop and maintain it. Research software sustainability is vitally important for the reproducibility of research outcomes and has a direct bearing on the process of research, including the efficient use of financial and human resources. Funders can play a crucial role in ensuring software sustainability by structurally supporting it. Over the past few years, a variety of methods for sustaining research software have been explored, including improving and extending funding policies and instruments. During an international workshop held on 8-9 November 2022 in Amsterdam, funding organizations joined forces to explore how to effectively contribute to making research software sustainable. This resulted in the Amsterdam Declaration on Funding Research Software Sustainability

There’s lots here on “sustainability” and even open-science standards (“as much as possible”), but not a word—nothing—about the commercial publishers’ acquire-and-stack software strategy.

Wired, reporting on an AI doctoral student’s recent paper:

The Abdallas examined the CVs of 135 computer science faculty who work on AI at the four schools, looking for indications that the researcher had received funding from one or more tech companies. For 52 of those, they couldn’t make a determination. Of the remaining 83 faculty, they found that 48, or 58 percent, had received funding such as a grant or a fellowship from one of 14 large technology companies: Alphabet, Amazon, Facebook, Microsoft, Apple, Nvidia, Intel, IBM, Huawei, Samsung, Uber, Alibaba, Element AI, or OpenAI. Among a smaller group of faculty that works on AI ethics, they also found that 58 percent of those had been funded by Big Tech. When any source of funding was included, including dual appointments, internships, and sabbaticals, 32 out of 33, or 97 percent), had financial ties to tech companies. “There are very few people that don’t have some sort of connection to Big Tech,” Abdalla says.

97 percent.