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Justices on the United States Supreme Court use rhetorical strategies to maintain institutional legitimacy. In the court opinion, a strategy called the monologic voice presents a flattering depiction of the Court. The monologic voice occurs through two tones, the individualistic and collective, which respectively maintain the Justices’ legitimacy through critique and the Court’s legitimacy through unification. We train large language models to identify these rhetorical features in 15,291 modern Supreme Court opinions, issued between 1946 and 2022. While the fraction of collective and individualistic tones has been relatively consistent between 1946 and 2022, the Rehnquist Court used the collective tone at a higher rate than any other Court. In recent terms, 2021 and 2022, we find suggestions of another rhetorical shift, as all Associate Justices of the Roberts Court, excluding Chief Justice Roberts, used the individualistic tone at a historically high rate. 

The representation of mobility in literary narratives has important implications for the cultural understanding of human movement and migration. In this paper, we introduce novel methods for measuring the physical mobility of literary characters through narrative space and time. We capture mobility through geographically defined space, as well as through generic locations such as homes, driveways, and forests. Using a dataset of over 13,000 books published in English since 1789, we observe significant "small world" effects in fictional narratives. Specifically, we find that fictional characters cover far less distance than their non-fictional counterparts; the pathways covered by fictional characters are highly formulaic and limited from a global perspective; and fiction exhibits a distinctive semantic investment in domestic and private places. Surprisingly, we do not find that characters' ascribed gender has a statistically significant effect on distance traveled, but it does influence the semantics of domesticity. 

Tracking characters and locations throughout a story can help improve the understanding of its plot structure. Prior research has analyzed characters and locations from text independently without grounding characters to their locations in narrative time. Here, we address this gap by proposing a new spatial relationship categorization task. The objective of the task is to assign a spatial relationship category for every character and location co-mention within a window of text, taking into consideration linguistic context, narrative tense, and temporal scope. To this end, we annotate spatial relationships in approximately 2500 book excerpts and train a model using contextual embeddings as features to predict these relationships. When applied to a set of books, this model allows us to test several hypotheses on mobility and domestic space, revealing that protagonists are more mobile than non-central characters and that women as characters tend to occupy more interior space than men. Overall, our work is the first step towards joint modeling and analysis of characters and places in narrative text.