Education Governance and Race: An Analysis of School Board Discourse Using Large Language Models

Abstract

Media accounts suggest that American school board meetings have transformed from bureaucratic affairs into arenas of conflict, particularly around race. Yet systematic evidence remains limited. Analyzing over 40,000 meeting minutes from 2018 to 2022, we examine the prevalence and predictors of race-related discourse. Using large language models and natural language processing, we find that race-related content appears in 24% of meetings, predominately voiced by officials. Opposition related to racial issues occurs in only 1% of meetings, coming from both officials and the public. Race-related discussions—both affirmative and oppositional—are most common in suburban, high-income, highly educated districts in the Northeast and West, where local discourse tracks national race-related discourse. Oppositional content also peaks in politically competitive areas.

Keywords

school boards U.S. school districts racial discourse education governance political polarization large language models natural language processing text classification

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