The Language of Law: An Analysis of Gender and Turn-Taking in U.S. Supreme Court Oral Arguments

Abstract

In this study, I present a corpus of short exchanges between speakers in U.S. Supreme Court Oral Arguments. Each exchange is labeled on a spectrum of “cooperative” to “competitive” by a human annotator with legal experience in the United States. To show the importance of this corpus, I analyze the relationship between speech features, the nature of exchanges, and the gender and role of the speakers. Finally, I train machine learning models with the corpus, and demonstrate that the models can be used to predict the label of an exchange with moderate success. The automatic classification of the nature of exchanges indicates that future studies of turn-taking in oral arguments can rely on larger, unlabeled corpora.