Painting (Inequality) By Numbers: Visualizing Gender Disparity in Literary Bibliographies

Among the nearly 100 selections in a prominent anthology of literary journalism—stories and excerpts from longer works—four were written by women. This glaring omission can be easily ascertained by the curious reader simply by knowing traditional gender associations of Western names and then counting. The ease of access to this information, however, does not in any way lessen its impact. Additionally, the simplicity of this method means it also works well at scale— counting was, after all, among the first tools in humanities computing. In this paper, I describe and employ a method for combining and mining traditional bibliographies, converting the information they contain into quantifiable data, predicting authors’ gender from historical data, and then, simply, counting. When visualized, this can be used to make shockingly visible the well-known gender disparities in literary canons. It’s one thing to talk about the dearth of represented women writers, it’s another to see it. In addition to an analysis of the predominant bibliographies in my sub-field of literary journalism studies, I will be crowdsourcing representative bibliographies from other literary sub-fields and subjecting them to this process of mining, text analysis, and visualization in an effort to create a series of striking visualizations illustrating the gender disparities across sub-fields of literary studies. This work is conducted with something of a wink—there are of course more complex (and complicated) computational methods than counting available to investigate gender disparity in literary canons, but sometimes an obvious problem requires an obvious method