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.

If I understand correctly, this book was recommended in the curriculum for Community of Christ Reunion camps this year; at least, I listened to it because it was recommended for the Reunion that I attended last weekend. I actually finished it on Monday, but it’s been a busy week, and so it’s taken me a while to write this review.

While I am an aspirational environmentalist, I’m not very in tune with nature, so I wasn’t sure how I’d like the book. That said, I really appreciated the author’s respectful challenging of dominant scientific paradigms, something that is professionally important to me. Her critique of consumerist greed also stood out to me. I got a lot from this book, and I think I’ll be blogging more about it in the future.

Woke up from a dream that I’d been charged with sharing results for federal grant applications from my college. Looked up myself, and was relieved to not get the grant that I’d applied for—but surprised to be awarded funds for a “bike book” project I didn’t remember proposing.

Back in December, I wrote a frustrated post about an article I’d submitted to a special issue that was now being repackaged into an edited volume, in which my research would appear as a chapter. At the time, I wrote about how frustrated I was at the lack of control I had over my own research output. I might well have consented to having my work reprinted in this new format, but I was frustrated that my consent was neither sought nor necessary for the process. I concluded that post with a quip that:

Let’s be honest: I’d much rather be at my department’s writing retreat today (with free lunch, to boot!) and that my kid be going to (definitely not free) summer camp. All that said, I’m deeply grateful that a professor’s schedule is flexible enough that I can respond to a sick kid pretty easily.

Gab’s been showing up more in the news lately, so I guess I should dust off some of that Gab data I have and move it closer to publication.

Too much truth in this.

This Saturday Morning Breakfast Cereal comic from yesterday spoke to me at a deep level:

My first thoughts went to generative AI, an area in which I feel like a fetishization of optimization is crowding out really important questions of what is good. As I put it in a university survey earlier today, there are undeniable benefits to the use of AI tools, but there are important questions as to who benefits. If my department started to use generative AI as a note-taking tool in faculty meetings (the specific focus of this survey), we would probably benefit from it!

Ben has been one of my best students over the past 5.5 years. He was a non-traditional student who flunked out of UK decades ago, went on to be a successful small business owner elsewhere in the country, and then leapt at the chance to come back to UK through an online degree completion program. As part of that program, he took one of the classes I was teaching at the time, which counted toward general education credit. Since then, he’s kept me in the loop on how things are going: He sent me an email to let me know when he graduated, we talked over the course of his MBA, and when he recently decided to start a PhD in his field of work, he wrote a letter to the university president, thanking the university for the chance to come back to school (and, generously, mentioning me by name in the letter).

Over the summer, I blogged about some concern that I had about a new research portal that my employer had just rolled out. Based on the gentle nudges to update our profiles we’ve been receiving since the platform’s launch, I’m guessing that faculty have not been as keen on the platform as the university is. One of those nudges came this week, and in the spirit of good faith cooperation, I spent some time going through the platform and updating my profile.

The headline obscures something important—that this is about research, access to data, and Terms of Service. Worrying stuff.