I’m sure I’m not the only one who finds the MLA Convention Program rather fascinating. The sedate-looking document is full of hidden stories about what our profession cares about, what we prioritize, and how our discipline is changing.
This is the first in a series of posts from the Committee on Information Technology on the 2018 MLA Convention Program and what it tells us about the state of DH today. In this particular post, I get the very fun job of identifying potentially relevant panels. Feel free to build your own lists and visualizations from the data.
The Spreadsheet: Finding Digital Work
One thing you might notice in this list is that I’ve opted for a very broad definition of “digital work,” rather than attempting to pull out a smaller list of panels of most interest to DH practitioners, as Mark Sample has done in the past in his useful posts. Partially, this is because my colleagues are planning to dig deeper into the specifics (as you’ll find out in subsequent blog posts). Also, I opted for breadth because it seems reasonable to assume that any panel mentioning “digital” is most likely engaging with a set of conceptual frames and questions that are relevant to DH practitioners, even if the panel is not solely or primary interested in the digital humanities.
So, how to find digital work sessions? First, there are a couple of built-in tags from the MLA that we can use. I started my spreadsheet with anything indicated as a session sponsored by the “TC Digital Humanities Forum,” and by my own committee, the Committee on Information Technology. I also added all sessions under the subject heading “Subject – Electronic Technology (Teaching, Research, and Theory).” With these tags, I created a list of 15 panels.
However, relying only on the MLA’s tags leaves out some obviously digital work-related sessions, as in, for example, “16. Digital Humanities in Practice: Caribbean Models,” (tagged “Comparative Literature: General”) and “415. ‘Aca- Fandom’ and Digital Scholarship: Rethinking Research and Fan Production” (tagged “Genre, Theory, Method: Cultural Studies, Folklore, and Popular Culture”). After experimenting with a few keyword searches, I added every session that includes the word “digital” to my list, bringing the total up to 54. I know this might be too expansive for some; I’ve indicated the decision making process on the spreadsheet’s second page so that readers can adjust their own lists accordingly.
Once the online version of the MLA program posted, I was able to mine the “Keywords” field to crosscheck digitally-relevant tags. Many of these confirmed sessions I had already found with the keyword search or MLA tags, and are indicated as “confirmed” on the spreadsheet. I also identified twelve additional panels with some variation of “digital humanities” or related keywords, bringing the grand total to 66 panels. For all the panels, I double-checked all relevant keywords in the hopes of finding additional panels and have indicated the results on the spreadsheet’s second page. (Note that the keywords are case-sensitive.)
You’ll notice that some panels are listed as “unconfirmed,” meaning I found them with a keyword search for “digital” but the online session description does not include any obviously digitally-relevant keywords. Some of these, I believe, clearly belong on this list; others I’m less sure about, but I’ve opted for the most expansive version. I’m not sure if session chairs are able to change their keywords, but these were current as of 11/17/17.
These panels range; some only obliquely mention the digital in one paper title, while others are clearly focused on DH questions. While not all of these are strictly speaking “digital humanities” panels, I believe that the whole set reflects the way that digital work, in various formats, has become integrated across our discipline.
While these 66 panels are the ones covered in the following visualizations, I’m conscious that this method can leave out other panels that might comfortably fit in my broad definition of Digital Work. The first page of my spreadsheet, therefore, will track additional crowd-sourced suggestions for other panels that might fit–I’ve already gotten some contributions. Please comment or Tweet me directly if there are additional panels that should be on this list. If there’s interest, I’ll update this post with the additional panels.
Networks
As I was reviewing my initial list of 66 panels, I was struck by how many different subdisciplines are represented, and I set out to visualize this range. In the version to the left, each of the center nodes represents one of the different official MLA tags, while the surrounding nodes represent the individual sessions. The nodes are colored based on tag category: blue for Genre, Theory, Method, purple for The Profession, red for Teaching, and green for all other tags (i.e., tags for different disciplinary categories).
Download the PDF here for better zooming.
The largest category, unsurprisingly, is the “Genre, Theory, Method: Electronic Technology” subject (unsurprising because I pulled in all sessions tagged with that category). However, all categories are well represented, and overall 22 separate subject tags are visible.
This isn’t a very complicated network (each node only connects to one other node), and so I used Gephi here more as a graphic design tool than as a network analysis tool. The Nodes and Edges tables I used are attached as pages of the main spreadsheet.
Plain-Text Versions
Since I almost always like starting with Voyant, I also prepped cleaned-up versions of the session descriptions. Download the Plain-Text Zip File here. These files are just the session and individual presentation titles, with author, time, and location info deleted.
The Voyant page for this corpus seems to me to show the importance of social media, public engagement, and project-based research to the field; I’m curious to hear what others make of it.
General Disclaimers and Thanks
Many thanks to Shawna Ross and the Committee on Information Technology for reading early drafts! Thanks to the Bryn Mawr College R Working Group, particularly Rachel Starry, for some fun collaboration getting a network in ggplot using this data. (It didn’t turn out great, but I’m happy to share this as well if anyone’s interested.)
Apologies for any typos in the data, particularly of names! I was working from the PDF version for most of this, and the OCR had a lot of strange errors around Ts and fs…
