Responding to Allington et. al’s argument that the digital humanities are a handmaiden to neoliberalism and non-progressive scholarship, Juliana Spahr, Richard So, and Andrew Piper respond that DH and progressive scholarship are not in fact incommensurable. Without getting too deep into the many contours of the debate, I want to suggest in this post what I think may be the hidden crux of the argument (though I doubt the authors of either essay would agree with me).
Spahr et al.:
Ultimately what has most troubled us about Allington et al’s essay is its final line, which is its core assertion: they call on colleagues in the humanities to resist the rise of the digital humanities. They have carefully studied the field of the digital humanities and declare that it must be shut down; nothing good can come from it. We worry about this foreclosing of possibility. Other academic disciplines, such as sociology, have benefited greatly from the merging of critical and computational modes of analysis, particularly in overturning entrenched notions of gender or racial difference based on subjective bias. We find it is too early to reject in toto the use of digital methods for the humanities.
The urgent questions articulated by “Neoliberal Tools” thus present a rich opportunity to think about the field’s methodological potential. Questions about the over-representation of white men or the disproportionate lack of politically progressive scholarship in the digital humanities regard inequality and have a strong empirical basis. As such, they cannot be fully answered using the critical toolbox of current humanistic scholarship. These concerns are potentially measurable, and in measuring them, the full immensity of their impact becomes increasingly discernable, and thus, answerable. The informed and critical use of quantitative and computational analysis would thus be one way to add to the disciplinary critique that the authors themselves wish to see.
In these final paragraphs, the authors make the move—an almost imperceptible move, but I think I can detect it—that anyone in the hard or social sciences must also make: they separate data from explanations for data. This, in my view, is what makes the ostensibly “progressive” or “activist” goals of some humanities scholarship somewhat incommensurable with computational work as such.
Questions about equality, the authors note, are questions that require large-scale measurement; they are not questions one can address adequately through close readings or selective anecdotes, which they describe as “the critical toolbox of current humanistic scholarship.” What they do not note—but I think it’s a point Allington et al. might eventually get around to making in a counter-argument—is that when you exchange a close, humanistic analysis for a data-driven one, then to a certain extent you relinquish control over the “correct” way to explain or theorize the resultant measurements. Indeed, our results, we now recognize, are far too easily “rationalized” with just-so stories that fit our pre-conceived notions (which isn’t to say that some just-so stories aren’t also true stories, or that some just so-stories aren’t truer than others).
“The data’s the data,” a biologist friend of mine once said. “It’s how you explain the data that gives rise to debates.”
Take Ted Underwood’s piece on gender representation in fiction, which Spahr et al. point to as an example of critical/computational scholarship. Underwood writes that between 1800 and 1989, the words associated with male vs. female characters are volatile and in fact become more volatile in the twentieth century, making it more difficult for models to predict whether a set of words is being applied to a male or a female. “Gender,” he concludes, “is not at all the same thing in 1980 that it was in 1840.”
“Ah, gender is fluid,” we might conclude. Solid computational evidence for feminist theory. But then Underwood makes the data-grounded move, noting that cause(s) of the trend are open to interpretation and further data exploration:
The convergence of all these lines on the right side of the graph helps explain why our models find gender harder and harder to predict: many of the words you might use to predict it are becoming less common (or becoming more evenly balanced between men and women — the graphs we’ve presented here don’t yet distinguish those two sorts of change.)
Whether previously gendered terms converge toward both male and female characters, or whether gender-predicting terms simply disappear in fiction, could very much make a difference from the standpoint of explanation, especially critical or political explanation. E.g., one could claim, given the latter case (disappearance of gender-predicting terms), that what we see at work is the ignoring of gender rather than the fluid reframing of it, an effect, say, of feminism on fiction but not in any sense a confirmation of the essential fluidity of gender. However, it would also be perfectly feasible to use either explanation to forward a more critical or activist-minded thesis. It could go either way. And there’s the rub. When you’re doing computational work, you cannot also at the same time be explaining your results. Explanation is step two, and it’s a step people can take in different directions, politically friendly, politically unfriendly, or politically neutral.
And if the computational work you’re doing is interesting, you should at least sometimes find things that overturn your preconceived notions. For example, Underwood notes that despite the general trend away from sharply-delineated gender descriptions, there are some important counter-trends.
On balance, that’s the prevailing trend. But there are also a few implicitly gendered forms of description that do increase. In particular, physical description becomes more important in fiction (Heuser and Le-Khac 2012).
And as writers spend more time describing their characters physically, some aspects of the body and dress also become more important as signifiers of gender. This isn’t a simple, monolithic process. There are parts of the body whose significance seems to peak at a certain date and then level off — like the masculine jaw, maybe peaking around 1950?
Other signifiers of masculinity — like the chest, and incidentally pockets — continue to become more and more important. For women, the “eyes” and “face” peak very markedly around 1890. But hair has rarely been more gendered (or bigger) than it was in the 1980s.
Rethinking things, perhaps we don’t see evidence that “gender is fluid” so much as evidence that gender remains sharply delineated, just along a different terminological axis than was previously the case. Or not. You could argue something else, too. Again, that’s the point.
As another example of what I’m talking about, we can look at Juliana Spahr’s and Stephanie Young’s work on the demographics of MFA and English PhD programs. It is an excellent piece, tied resolutely to statistics, but it ends this way:
We have ended this article many different ways, made various arguments about what is or what might be done. These arguments now seem either inadequate (reformist) or unrealistic (smash the MFA, the AWP, the private foundations, the state). At moments we struggled with our own structural positions even as these structures were created without our consent but to our advantage . . .
. . . we agree with McGurl when he argues that “[w)hat is needed now […] are studies that take the rise and spread of the creative writing program not as an occasion for praise or lamentation but as an established fact in need of historical interpretation: how, why, and to what end has the writing program reorganized U.S. literary production in the postwar period?” For us, for now, the best we can do is work to understand so that, when we create alternatives to the program, they do not amplify its hierarchies.
More research needed, in other words. Any previous calls to activism muted.
Spahr and Young do a wonderful job compiling relevant demographic information, but in so doing, they rightly recognize that interpreting the information (both historically and in the present moment) is another job altogether. The data are separated from their explanation. Spahr and Young are, I imagine, on the political left, but their data remain open to explanation from multiple political or apolitical perspectives.
From an apolitical perspective, I would want to explain some of their demographic data with simple demography. For example, they imply that 29% non-white representation in English PhD programs is not enough, but America is precisely 29% non-white and 71% white, so I don’t find that statistic problematic at all. I would also claim that this same demographic point partially ameliorates the 18% non-white representation in MFA programs, though obviously, a gap in representation remains. How to explain it, though? Their essay is (rightly) not ideological enough to foreclose on all but a single, left-facing window of possibility. This is a good thing. Recognizing the possibility of multiple explanations is what keeps a field of inquiry from becoming an ideological echo-chamber.
Spahr et al. also point to sociology as a field that uses computational methods to address critical, cultural questions. But again, addressing critical or cultural questions with computational methods is not at all the same thing as being critical, culturally progressive, or activist. Sociologists (and psychologists) have, I think, always recognized, if only quietly, that progressive or activist readings of their data are by no means the only readings. Steven Pinker and Jon Haidt, among others, are really pushing the point lately with their Heterodox Academy. It’s all a big debate, of course, but that’s the point.
In my view, good computational scholarship opens up debate and rarely points to One Single And Obvious And You’re Stupid If You Don’t Believe It conclusion. Sometimes it does, but that’s usually in the context of not-immediately-political content (e.g., whether or not Piraha possesses recursive syntax). But when you’re talking about large social or political explanations, I’ve never seen the explanation that doesn’t leave me thinking: Mm. Maybe. Interesting. I dunno. We’ll see.
I’m sure my skepticism comes across as conservatism to some. From my perspective as a scholar, however, I’m simply tentative about my own worldview. I’m therefore deeply suspicious of any scholar or study purporting to provide 100% support for any particular ideology or political platform. So I think it’s a good thing that a lot of DH work doesn’t do that. Indeed, I’m drawn most often to theories that piss off everyone across the political spectrum—e.g., Gregory Clark’s work—because my most deeply held prior is that the world as it is probably won’t conform very often to any particular ideology or politics. If anything, then, I’d like to see more DH work not confirming a single orthodoxy but challenging many orthodoxies all at once. Then I’ll be confident it’s doing something right.