What Is Literary Studies?
This is the question that underpins Da’s takedown of what she calls computational literary studies (CLS). The animus with which she pursues this essay is like a search light that creates a shadow behind it. “The discipline is about reducing reductionism,” she writes (p. 638), which is a questionable assertion about a field that encompasses many kinds of reduction and contradictory epistemic positions, from thing theory to animal studies. Da offers no evidence or authority to back up her contention that CLS fails to validate its claims. Being charitable, what Da means, I think, is that literary scholars should always attend to context, to the particulars of the works they engage.
Da’s essay assails what she terms the false rigor of CLS: the obsession with reductive analyses of large datasets, the misapplied statistical methods, the failure to disentangle artifacts of measurement from significant results. And there may be validity to these claims: some researchers use black box tools they don’t understand, not just in the digital humanities but in fields from political science to medicine. The most helpful contribution of Da’s article is tucked away in the online appendix, where she suggests a very good set of peer review and publication guidelines for DH work. I can imagine a version of this essay that culminated with those guidelines rather than the suggestion that “reading literature well” is a bridge too far for computational approaches.
The problem with the spotlight Da shines on the rigor of CLS is that shadow looming behind it. What does rigor look like in “the discipline” of literary studies, which is defined so antagonistically to CLS here? What are the standards of peer review that ensure literary scholarship validates its methods, particularly when it draws those methods from other disciplines? Nobody is calling in economists to assess the validity of Marxist literary analysis, or cognitive psychologists to check applications of affect theory, and it’s hard to imagine that scholars would accept the disciplinary authority of those critics. I am willing to bet Critical Inquiry’s peer review process for Da’s article did not include federal grants program officers, university administrators, or scholars of public policy being asked to assess Da’s rhetorical—but central—question “of why we need ‘labs’ or the exorbitant funding that CLS has garnered” (p. 603).
I contend this is actually a good idea: literary studies can benefit from true dialog and collaboration with fields across the entire academy. Da clearly feels that this is justified in the case of CLS, where she calls for more statistical expertise (and brings in a statistician to guide her analysis in this paper). But why should CLS be singled out for this kind of treatment?
Either one accepts that rigor sometimes demands literary studies should embrace expertise from other fields—like Da bringing in a statistician to validate her findings for this paper—or one accepts that literary studies is made up of many contradictory methods and that “the discipline” is founded on borrowing methods from other fields without any obligation validate findings by the standards of those other fields. What would it look like to generalize Da’s proposals for peer review to other areas of literary studies? The contemporary research I find most compelling makes this more generous move: bringing scholars in the humanities together with researchers in the social sciences, the arts, medicine, and other arenas where people can actually learn from one another and do new kinds of work.
To me, literary studies is the practice of reading and writing in order to better understand the human condition. And the condition is changing. Most of what we read now comes to us on screens that are watching us as we watch them. Many of the things we think about have been curated and lobbed into our consciousness by algorithmic feeds and filters. I studied Amazon recommendation networks because they play an important role in contemporary American literary reception and the lived experience of fiction for millions of readers—at least circa 2010, when I wrote the article. My approach in that work hewed to math that I understand and a scale of information that I call small data because it approximates the headspace of actual readers thinking about particular books. Small data always leads back to the qualitative and to the particular, and it is a minor example of the contributions humanists can make beyond the boundaries of “the discipline.”
We desperately need the humanities to survive the next century, when so many of our species’ bad bets are coming home to roost. Text mining is not “ethically neutral,” as Da gobsmackingly argues (p. 620), any more than industrialization was ethically neutral, or the NSA using network analysis to track suspected terrorists (Da’s example of a presumably acceptable “operationalizable end” for social network analysis) (p. 632). The principle of charity would, I hope, preclude Da’s shortsighted framing of what matters in literary studies, and it would open doors to other fields like computer science where many researchers are, either unwittingly or uncaringly, deploying words like human and read and write with the same kind of facile dismissal of methods outside “the discipline” that are on display here. That is the context in which we read and think about literature now, and if we want to “read literature well,” we need to bring the insights of literary study to broader conversations where we participate, share, educate, and learn.
ED FINN is the founding director of the Center for Science and the Imagination at Arizona State University where he is an associate professor in the School of Arts, Media, and Engineering and the Department of English.