Rachel Sagner Buurma, Swarthmore College
Scholars tend to assume that the use of quantitative methods in the study of literary or cultural texts is a relatively new phenomenon. This is not quite true; throughout the course of the twentieth century literary scholars (such as Edith Rickert, Caroline Spurgeon, and Josephine Miles, for example) have turned to quantitative methods to help them make arguments about literature and culture. Despite this long history, however, until recently literary scholars have primarily drawn on statistical descriptions of corpora rather than statistical models of them, and the recent turn to using statistical models to understand literary corpora and literary history has opened new possibilities for literary study. As Andrew Piper writes, models offer not just new views of literary history and literary texts, but because they are iterative, sharable, and necessarily never perfectly fitted they “open the door to new kinds of critical sociability” (“Think Small: On Literary Modeling,” PMLA 132.3, 657). However, though the use of the statistical models is relatively new to literary studies, in fact the discipline has long had another resource for thinking about modeling: the novel. In this paper I will consider the affordances and limits of using the methods of modeling social life that Victorian novels developed over the course of the nineteenth century to help us think about and theorize statistical models. Turning to the work of novel theorists and theorists of fictionality to help me describe the Victorian novel’s own theory of modeling, I will ask how the history of novel reading can help us understand our reading of models as representational objects with productively reductive relationships to the worlds from which they draw their data. To put it briefly: how does the way we read (and have read) novels inform the way we read (and have read) models?
No extended abstract or paper available
Presented in Session 34. Big Data and Its Discontents: Assumptions about Reading History in the Automated Analysis of Texts