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Abstract
Selecting an appropriate method of clustering for network data a priori can be a frustrating and confusing process. To address the problem we build on an a posteriori approach developed by Grimmer and King (2011) that compares hundreds of possible clustering methods at once through concise and intuitive visualization. We adapt this general method to the context of social networks, extend it with additional visualization features designed to enhance interpretability, and describe its principled use, outlining steps for selecting a class of methods to compare, interpreting visual output, and making a final selection. The interactive method, implemented in R, is demonstrated using Zachary's karate club, a canonical dataset from the network literature.

The extent to which judicial outcomes depend on judges' identities is a central question in multiethnic societies. Past work on the impact of the racial composition of appellate courts has narrowly focused on civil rights cases in the United States. We expand this literature by testing for ethnicity‐based panel effects in criminal appeals in Israel. Using randomness in the assignment of cases to panels, we find that appeal outcomes for Jewish defendants are independent of panels' ethnic composition. By contrast, panel composition is highly consequential for Arab defendants, who receive more lenient punishments when their case is heard by a panel that includes at least one Arab judge, compared to all‐Jewish panels. The magnitude of these effects is sizable: a 14–20% reduction in incarceration and a 15–26% reduction in prison sentencing. These findings contribute to recent debates on the relationship between descriptive representation and substantive outcomes in judicial bodies.