Bridging research in social psychology with scholarship on racialized organizations, this article shows how individual bias and organizational demographic composition can operate together to shape the degree of discrimination in schools. To understand Black and Latino boys’ higher rates of discipline that persist net of differences in behavior, I combine an original video experiment involving 1,339 teachers in 295 U.S. schools with organizational data on school racial/ethnic and socioeconomic composition. In the experiment, teachers view and respond to a randomly assigned video of a White, Black, or Latino boy committing identical, routine classroom misbehavior. I find that, compared to White boys, Black and Latino boys face a double jeopardy. They experience both (1) individual-level teacher bias, where they are perceived as being more “blameworthy” and referred more readily for identical misbehavior, and (2) racialized organizational climates of heightened blaming, where students of all races/ethnicities are perceived as being more “blameworthy” for identical misbehavior in schools with large minority populations versus in predominantly White schools. This study develops a more comprehensive understanding of the production of racial/ethnic inequality in school discipline by empirically identifying a dual process that involves both individual teacher bias and heightened blaming that is related to minority organizational composition.

The decision to engage in military conflict is shaped by many factors, including state- and dyad-level characteristics as well as the state’s membership in geopolitical coalitions. Supporters of the democratic peace theory, for example, hypothesize that the community of democratic states is less likely to wage war with each other. Such theories explain the ways in which nodal and dyadic characteristics affect the evolution of conflict patterns over time via their effects on group memberships. To test these arguments, we develop a dynamic model of network data by combining a hidden Markov model with a mixed-membership stochastic blockmodel that identifies latent groups underlying the network structure. Unlike existing models, we incorporate covariates that predict dynamic node memberships in latent groups as well as the direct formation of edges between dyads. While prior substantive research often assumes the decision to engage in international militarized conflict is independent across states and static over time, we demonstrate that conflict is driven by states’ evolving membership in geopolitical blocs. Our analysis of militarized disputes from 1816 to 2010 identifies two distinct blocs of democratic states, only one of which exhibits unusually low rates of conflict. Changes in monadic covariates like democracy shift states between coalitions, making some states more pacific but others more belligerent. Supplementary materials for this article are available online.