The Language of Closure: Examining Racial Differences in How A Community Discusses School Closure Metrics

School closures in urban districts disproportionately affect marginalized communities, yet community input often goes unanalyzed or is reduced to simple frequency counts. This study applies BERTopic, a neural topic modeling approach, to analyze 4,159 suggestions from 2,006 community members regarding school closure metrics in a large urban district. Through extensive hyperparameter tuning across 62 configurations, we identified 14 coherent topics that capture community priorities. Chi-square analysis revealed substantial variation in topic prioritization by race (χ2 = 152.0825, p < 0.0001, V = 0.1439). Furthermore, an analysis of topic outliers revealed that White respondents were significantly more likely to provide suggestions that fell outside of community-wide themes (z = 2.14). These findings demonstrate that ”neutral” community engagement processes may obscure the specific concerns of marginalized groups, and highlight the utility of computational methods in surfacing rigorous insights from large-scale text data.