Colloquium on Party Polarization in the

This research colloquium examines party polarization in the United States: how it has been conceptualized and measured, what causes and consequences scholars have identified, and–just as importantly–where the concept’s explanatory reach ends. Polarization has become a fashionable diagnosis for nearly everything that troubles American democracy; part of our task is to take the concept seriously enough to see where it has been stretched thin, conflated with adjacent phenomena (e.g., trait-based and geographic sorting, populism), or mistaken for something else entirely.

The colloquium is built around a semester-long case study. Each student adopts a U.S. state (New York is reserved as the whole class’s shared anchor case and teaching example) and uses it as a running laboratory for the concepts, data, and methods we cover. Class time alternates between discussion of assigned readings and hands-on data analysis using R. Six scaffolded components–a background profile, a polarization diagnosis, a geographic sorting analysis, a trait-based sorting analysis, an impact analysis, and a causes analysis (the last aided by a three-week AI-assisted media project)–build on each other over the course of the semester and are then assembled and revised into a final research paper, presented to the class in the last two weeks.

The course mixes traditional and AI-assisted work. Early in the semester, you’ll do original, manual writing and coding–independent of AI–to build fluency, voice, and critical thinking skills. Later, we incorporate AI tools deliberately, to (1) facilitate a more ambitious scope of analysis than would otherwise be possible in a single semester; and (2) develop judgment in using AI as a collaborator that can sharpen and enhance thinking and analysis, rather than substitute for these critical skills. As we incorporate AI, we will work on critically evaluating its output and disclosing its use appropriately.