Computing the Formal and Institutional Boundaries of Contemporary Genre and Literary Fiction

Though the concept of genre has been a subject of discussion for millenia, the relatively recent emergence of genre fiction has added a new layer to this ongoing conversation. While more traditional perspectives on genre have emphasized form, contemporary scholarship has invoked both formal and institutional characteristics in its taxonomy of genre, genre fiction, and literary fiction. This project explores the soundness of genre as a formal designation, using computational methods to identify the prevalence of narrative features both within specific genres and within genre versus literary fiction at large, and examining how this varies with author gender. Pulling from Andrew Piper’s CONLIT dataset of Contemporary Literature, we assemble a corpus of literary and genre fiction, with the latter category containing romance, mystery, and science fiction novels. We calculate the distribution of narrative features according to author gender within each genre and within genre versus literary fiction at large. We then use logistic regression to model the effect that each feature has on literary classification and to measure how author gender moderates these effects. This project finds statistically significant formal markers of each literary category and highlights the unexpected ways in which author gender impacts literary classification.