This post is missing the original images because I’ve moved this blog around way too much. Sorry. You’ll have to take my word for it, I guess.

I work with a pretty smart data science guy. In the past, when the MLA Conference program hit online, I would enter in words like “teaching” and “pedagogy” and “adjunct” into the search bar and see just how many panels/talks were on these topics. But inspired by Kris, I decided to try and do something more systemic with the data I was finding.

It helped that the MLA has a database of all the conference programs online dating back to 2004. So I made a (really ugly) spreadsheet. And then, I decided I needed a data set to compare the MLA numbers to. Ideally, I wanted to compare the MLA conference with the CCCC, but the CCCC only seem to have archived their conference program in PDF format (all differently formatted as well, but more on that later). Instead, I had to settle for the AHA conference as the point of comparison. The AHA online conference database only goes back to 2009, so it’s not a perfect comparison, but it worked for my simplistic purposes.

Methodology

Quantitative researchers, please don’t get too mad at me. This was rudimentary research which involved entering in a search term in the search bar and just recording the number of results. I got the total number of session from the MLA by their own counting system (ie scrolling to the last session on the last day and taking down that number). The AHA was a bit trickier because they don’t count all of the various allied sessions in their total. So I used the AHA total number and then hand-counted the unnumbered allied sessions. I excluded the entries on this list that weren’t sessions (dinners, etc) which I know the MLA counts, so again, it’s not apples to apples, but the percentages are so low that at the end of the day, it won’t make that much of a difference for my overall thesis.

Wait, what is that thesis (that my friends, is called burying the lede)? My thesis is that the MLA conference doesn’t center issues of the most interest and that are most pressing for the (imaginary) membership: teaching and contingent faculty issues. My secondary thesis was that the CCCC did a better job, and maybe someday I’ll be able to test that thesis, but for now, I had to see if the historians faired any better than my colleagues in the broad fields that make up the Modern Languages.

Some weirdness in my numbers – first, the MLA didn’t have a conference in 2010 because they moved the dates from December to January. Also, there is something odd about the 2015 teaching numbers for the AHA, but I couldn’t figure out how to disaggregate what I was actually looking for.

Conclusion? History does WAY better talking about teaching than the MLA does.

I had originally included “labor” as a search term, but the AHA numbers really mess up the data visualization because, I imagine, historians are more interested more generally about labor (and the history thereof) than language and literature people. And that isn’t to say that historians are talking about the labor conditions of their colleagues in the academy. So, you can see the raw numbers on the spreadsheet, but for the purpose of a less-ugly bar graph, I’ve only included “adjunct” and “contingent”.

As you can see, it’s all over the map, and “contingent” is a much more popular word than “adjunct.” But it makes up a minuscule percentage of the total number of panels at the conference. We’re still not talking about it. Not much.

Now, of course, there are TONS of limitations on this kind of simplistic and brutalist research. If the data was in better condition, I could do some topic modeling and understand more precisely the kinds of things that are being said in these talks and presentations, at least based on the panel and paper descriptions. Also, the inconsistent formats of the data (and the inconsistent search engines) make it hard to do a true apples to apples comparison.

I know that the MLA has done some great work to make their data more available (and the full MLA conference programs are available in their PMLA format, which has remained extraordinarily consistent), and Kris, inspired by me, has done some more in-depth work in mining the data. But, I’m hoping that organizations like AHA and CCCC do some more work to make their data more available for this kind of investigation. It’s important to understand how our disciplines have evolved (or not) especially as it concerns our most important and pressing issues.

So, if you have a better data set, or more data sets, or some influence, please let me know. And feel free to use whatever data I’ve collected for whatever you want to. It’s just a rough start. It might be as far as I ever get with it. But I hope I’ve inspired some people to ask these questions and come up with better methodologies and answers.