Mathematics Scan
Category: Teacher and Leader Development
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.
Keywords
School Closures, Racial Equity, Natural Language Processing (NLP), BERTopic, Educational Governance, Urban Education
Education level
Document Object Identifier (DOI)
10.26300/dfry-5906
