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Education has long been a polarizing issue in American politics. In recent years, polarization has centered around issues of diversity and inclusion, with conservative movements and so-called parents’ rights organizations increasingly targeting racial, ethnic, and LGBTQ+ representation in education. Following the 2020 COVID pandemic, a new strain of anti-science sentiment has also flourished. Despite this societal contention, systematic analyses of the factors that drive both cultural disputes and regressive stances on these key issues remain elusive. This paper examines the community-level “ecological” predictors of positions adopted by winning candidates in school board elections held in November 2022. Specifically, we analyze the conditions under which winning candidates: (1) took a stance (for or against, versus no stance) on a contentious issue related to education; and (2) amongst those who
took stances, their position toward this issue. We do so for three particularly contentious issues: the inclusion of topics related to race and ethnicity in school curricula – often termed “Critical Race Theory” (or CRT) – the inclusion of topics related to sexuality and gender identity in school curricula, and the implementation of COVID-19 lockdown measures and vaccination requirements. In doing so, we draw on rich sociological literature on status, threat, and social trust. To examine these relationships, we leverage a series of random forest classification models – an ensemble machine learning method that enables us to incorporate multiple interrelated variables simultaneously, assess their relative importance, and delineate complex patterns of association. This study will contribute not only to a deeper understanding of the dynamics shaping the ongoing battle for education, but also to scholarship at the intersection of political and cultural sociology through a holistic, exploratory, and ecological approach to the interrelated dynamics of backlash, status, threat, and social trust. Additionally, our novel application of random forest classification models demonstrates the applicability of a relatively underutilized methodological approach, one that is arguably better suited to unpack the complexities of local contention than typical modeling strategies and is, therefore, promising in both its potential empirical insight and theoretically generative qualities.