When I attended the 4Cs a couple of weeks ago, I felt that I really went to two conferences. The first was the Research Network Forum (RNF), a pre-conference workshop in which scholars with similar interests are organized into small groups, with whom they discuss their ongoing research and get feedback and recommendations for sources and avenues of exploration. Though attendees are matched up by the workshop organizers, the RNF still has something of an unconfrence feel in that like-minded scholars simply get to talk about what they’re interested in and start a real conversation with others.
What also set the RNF apart for me were the keynote speakers, specifically Rebecca Moore Howard and Sandra Jamieson, the Principal Researchers for the Citation Project. Their talk, “Take a Deep Breath and Jump: Doing Data-Driven Research When you Aren’t Trained in Data-Driven Methods,” discussed the trials and tribulations of working with statisticians (and IRB) to most accurately the results of their findings. Moore also discussed her transformation from a rhet/comp scholar who based her arguments largely on classroom observation to one who based her claims on data, and, with the Citation Project, big data.
The other conference, then, was the Cs itself: a good deal of one-way paper-giving, some highlights, some lowlights, and some well-travelled paths of research. It was because of the RNF, though, that I specifically noted the lack of panels or featured talks about data – big data, data mining, and, perhaps most conspicuous of all, dedicated Digital Humanities topics.
It’s impossible to know the whole content of the conference, all the panels, and even individual papers (they can go SO far off what their titles suggest), but searching through the program, only the RNF keynote spoke of data-driven research specifically. Another panel, “Gateways for Methodology: Report on a Summer Seminar for Building Disciplinary Research Capacity,” ostensbily hits on the topic some, especially with Charles Bazerman’s paper, “The Need for Building Research Capacity in Writing and Composition Studies.”
It was noticable, too, that only one panel at the Cs had “Digital Humanities” in the title, despite DH’s increasing attention and relevance at other conferences, especially the MLA. (NB: at least one paper outside of that panel had “Digital Humanities” in its title, and several other papers had some relevance to the field, including one on data mining.)
In the one clearly dedicated DH panel, “New Gateways for Research: Digital humanities and Writing Studies,” Matthew Gold spoke of the long-held (or perceived) split between Composition Studies and Literary Studies, the latter of which has been a comfy home for DH scholarship. Composition Studies, despite being housed in the English department, doesn’t think of itself as literary, Gold argued. And he’s right – Composition Studies probably doesn’t even think of itself as the Humanities.
And so it was very noticable to me that, despite the increasing need for and ability to do data-driven research in the Humanities, the Cs seemed to mostly miss the boat – whether by the choice and selectivity of the conference organizers or by the collective absence of Comp/Rhet scholars who have that focus, I’m not sure. That a keynote or featured speaker on big data belonged only in a pre-conference workshop (though – irony – the workshop was dedicated to research topics and methods) seems to say a lot.
To say that there’s a data “problem,” of course, insinuates that what’s in the program now isn’t quite right, or not good enough, and I don’t want to necessarily suggest that; Rhet/Comp gets great strength from having a wide variety of interestes represetned at its conference. But William Hart-Davidson, the DH session’s chair, was right when he closed with an inceasingly common – but neverthesless exigent – call for increasing the amount of digital studies (and DH work specifically) in a world inevitably swept up in technology. Digital methods and big data can better speak the language of disciplines outside of the Humanities, and can often foster more collabortaion (one of DH’s core values).
Maybe I shouldn’t be at all surprised by this – both the “social turn” in rhet/comp as well as post-process theory suggest that rhetoric and writing are processes so amorphous that science and its methods can’t possibly help describe their workings. To that effect, studies of writing and cognition were booted as well, leaving studies of how students actually perceive and process writing largely to psychology departments.
I’ll write more ehre about what I think is the necessity for bringing contemporary studies of cogniiton back into writing research, but to me, it’s clear already that comp/rhet’s shying away (or disdain for) data-driven methods only serves to keep it in its already well-formed silo(s). To truly bust out into more disciplines and make studies of writing matter beyond the immediate discipline, data-driven methods are invaluable, and it certainly seems that DH – as the discipline in the Humantieis that deals with big data – is well-poised to make inroads down the way.