Si on doit absolument investir au niveau national dans l’IA, je préfère cette approche (investir dans les recherches).

I’ve repeatedly referenced 20th century French technology scholar Jacques Ellul on my blog(s) since the beginning of the year. While my interest in Ellul’s work is also personal and political, I wrote back in February that one of the main reasons I’m reading a lot of Ellul right now is to add a stronger theoretical foundation to my scholarly work.

With that context in mind, I’m happy to share that my first Ellul-inspired article has just been published in the Journal of Computing and Higher Education! After I wrote this post on what Ellul had to say about the value of research, Stephanie Moore was kind enough to invite me to expand my thoughts there into a contribution for a special issue of that journal that she was putting together on “The Research We Need” in educational technology.

That feeling when you write an article for a lesser journal’s special issue but actually find a home in a stronger journal after the special editors reject it.

I’m pleased to be able to finally share the publication in the British Journal of Educational Technology of an article that Dan Krutka and I have been working on for some time, which documents activity in a teachers’ group on a far-right social media platform (which we intentionally don’t identify within the paper). Here’s a link to a full-text, read-only version of the article, and here’s the abstract as a preview:

One of the most frustrating experiences I’ve ever had with a journal appears to be coming to a happy conclusion, and that’s not a bad way to start a Monday.

I don’t personally need research funding, but I work in an academic ecosystem that’s highly dependent on it. Things aren’t looking good.

Last month, I wrote on both my reading up on Jacques Ellul and on concerns about how we understand the purpose and value of research. I’m continuing to read—or, rather, listen to—Ellul’s The Technological Society, and I was interested to find a passage that brought together these two ideas. Here’s Ellul, writing in the mid-twentieth century:

We have already examined the requirement of immediate applicability; here we meet it again on the state level. The state is not disinterested any more than private capitalists, but it is concerned in a different way. The state claims to represent the public interest and hence to have the duty of being a “good manager,” dispensing the public revenues only on condition that they mean something, that they pay off. Disinterested activity on the part of the state is inconceivable. Some may such that such activity should not be impossible; but in fact it is impossible. Neither individuals nor public opinion nor the structure of the state is oriented toward the acceptance of the kind of culture pure scientific research would represent.

Thanks to this article from The Atlantic that I saw on Bluesky, I’ve been able to confirm something that I’ve long assumed to be the case: that my creative and scholarly work is being used to train generative AI tools. More specifically, I used the searchable database embedded in the article to search for myself and find that at least eight of my articles (plus two corrections) are available in the LibGen pirate library—which means that they were almost certainly used by Meta to train their Llama LLM.

In the past 24 hours, I’ve seen two different CFPs that apply perfectly to a paper I wrote and then abandoned after a rejection and a paper I’ve wanted to write but had no good reason to. This may be what gets me back into a research groove this semester.

My stress and anxiety levels have been high ever since the second Trump administration began and immediately started taking an axe to all sorts of things that one should not take an axe to. For admittedly selfish reasons, though, I’ve been particularly anxious since Friday, when the NIH announced that it was dramatically cutting its support to universities (and other research institutions) in the form of indirect costs. I don’t do NIH-funded work, but we’re a very medically focused campus, and there’s no way that the $40 million that the University estimates we could lose over the next year isn’t going to have ripple effects across campus (not to mention the fact that my colleagues in the College of Communication and Information regularly look to the NIH as a source of funding health communication research). There are much more vulnerable populations currently being targeted by the Trump administration, and their concerns are more salient than mine are right now, but this is one of the administration’s decisions that’s hit closest to home, and I’ve been thinking a lot about it recently.

Me, all of December: “Next semester, I have a course release and a once-a-week evening class. I’m going to get so much research done during the day!”

Me, on February 7th: “Wow, it’s great to finally be getting to research this semester.”

I’m proud of the research I’ve done on online communities taking cues from dark corners of the internet, and I’m glad that some reporters find it interesting. It’s still uncomfortable to read certain excerpts from my data out loud to people over the phone.

Reread some feedback from a journal editor after a couple of days, and while I still disagree with it, it’s at least more reasonable than I remembered it being.

For the past couple of years, my colleague Dr. Meghan Dowell and I have been working on a paper on students’ awareness of what data the Canvas learning management system collects (and subsequently makes available to certain stakeholders). I’m a fan of Nick Proferes’s paper [Information Flow Solipsism in an Exploratory Study of Beliefs About Twitter] and have long wanted to do something similar related to LMSs. This is even more Meghan’s area of specialty than mine, though, so I was grateful that she was also interested in the subject and took the lead in turning this idea into reality.

I love having a co-author who can provide the theoretical framing to turn my weird data from a dark corner of the internet into an interesting argument.

Back in 2020-2021, I made the decision to pivot from Twitter as a research site in case data ever became less available—and so I could focus more on right-wing online spaces. It was a good call but still hilariously mistaken at the same time.

Before writing what I want to write, I want to make a few things clear. Teaching is an important and noble profession, I love being a teacher, and it’s possible (and often easy) to distinguish between better and worse ways of teaching. With that out of the way, I want to start off this post by arguing that teaching is less of “a thing” than learning is. That is, learning is the real phenomenon here, and teaching is sort of an auxiliary practice that aims to support learning but can’t ever quite be the same thing.

That feeling when data you’re coding is just so bizarre that you don’t know what to make of it. Online communities are a heck of a thing to study.

What a gross offer to receive.

I usually do too much qualitative work for them to be useful, but .Rmd-based slides are a delight to work in.

I have conflicted feelings about productivity books, but even as I increasingly reject the emphasis on productivity, I do find that there are some gems in these books that are helpful to me as I try to keep my life organized across all of its dimensions. While rereading one of these books over the summer, I came across the following quote (which appears to be a misquotation of Oliver Wendell Holmes, Jr.):

I keep thinking about the similarity of exploitation of academic labor by publishers to the exploitation of everyone’s labor by AI companies, and stories like this just make it more clear.

I dedicated most of my early career to Twitter and probably owe my tenure to the ease of collecting Twitter data once upon a time. Were it not for some timely decisions to diversify what platforms I was looking at, the API cutoff documented in this book would have really messed me up.

Because of how important Twitter was to me professionally, I followed a lot of this news as it was happening. Somehow, though, remembering all of these facts and events as I read them all in one place felt overwhelming. Had Musk really created that much chaos in such a relatively short period of time?

Interesting read wirh important implications for how we think about research quality.

Interesting—and hopeful—read.