Cultural Analytics Now
In his introduction to this cluster, Dan wrote that cultural analytics "has arrived at a new dispensation," catalyzed by the publication of A World of Fiction, Enumerations, and Distant Horizons. This new phase is marked by a turn away from the myths of algorithmic neutrality and empirical objectivity, and toward humanistic modeling. With this most recent instantiation, the stakes of the cultural analytic project extend beyond English departments and the humanities, offering a major intervention in data science. The lively debates that these three books have sparked in this cluster are an indication of this new phase's thrilling promise.
This is not to say that practitioners are unified in their vision. In one view, CA should be more expansive, providing comprehensive evidence to support the large-scale literary historical claims that we critics make. But in another view, cultural analytics should contract, get more specific and more nuanced about its objects of study and the types claims it can make. All three of these scholars insist that this new phase move away from the totalizing impulses that marked the field at its most Morettian. That is, while Piper, Underwood, and Bode all advocate for the use of models, they insist upon the fact that models are contextualized and contingent.
To close out this cluster, I offer three generalizations (unscientifically sampled) about the new landscape that Piper, Underwood, and Bode chart for the field. These are, from my perspective, the most exciting provocations from each book, ideas that invite us to imagine what cultural analytics might be next.
Implicatedness
When we use numbers to understand culture, we need not accept crude empiricism or sacrifice humanistic commitments to interpretation. In fact, to do so would be impossible. Because models mediate between individual observations and objects of study, they always encode subjectivity — not only who we are, not only our assumptions, biases, and commitments, but also a particular moment of scholarly discourse and our position in relation to institutions. This is the model builder's greatest asset.
Perspectives
When we acknowledge the perspectives enmeshed in human-made models, we can uncover literary history's contested grounds—points of competition, opposition, or difference, ways in which meaning is made differently. By adopting Underwood's method of perspectival modeling, we might think like science fiction or think like a detective novel. Said another way, models are biased and that's the point: we must put these subjective models to use to understand difference and complexity.
Systems
When we recognize that models are interpretive, and that they can capture a multitude of perspectives, we can use them to understand the literary field in its fullness: an interlocking, co-dependent world literary system. We can model the perspectives of any number of agents who operate within this system, not just the producers — editors and translators, distributors and readers. To do so, we must think more holistically and rigorously about our practices of data curation and management.
Bode, Underwood, and Piper have set an ambitious trajectory for the field. Taken together, these books envision an expansive future for cultural analytics. That future is more humanistic, more rigorous, more historical. I, for one, am excited to watch the field continue to refashion itself—to join these three scholars, and the fierce group of junior scholars coming up alongside me, in testing assumptions, considering more complex variables, and experimenting with new ways to think about, practice, and model cultural analytics.
Laura B. McGrath is Associate Director of the Stanford University Literary Lab and, beginning in 2020, Assistant Professor of English at Temple University.
