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Gender Inequality Beyond the Gender Binary: Recasting How Gender Shaped American Schooling, 1960 – 2012

Sat, August 9, 2:00 to 3:30pm, Swissotel, Floor: Concourse Level, Zurich A

Abstract

The rapidly expanding gender gap in American higher education has refocused attention on the underachievement of boys. Although sociologists have long argued that boys’ academic engagement is undercut by dominant masculinity norms, these norms have been largely invisible in quantitative research. Instead, researchers have been restricted to documenting disparities by binary sex, collapsing the entire spectrum of gender diversity into a 0 or 1. Moving beyond this binary approach, the current study uses contemporary machine learning techniques to inductively measure gender norms across five decades of American high school cohort studies. Analyzing six separate cohorts, I establish adherence to dominant gender norms as an independent and highly consequential axis of educational inequality. Whereas the academic impact of femininity norms has proven quite malleable over the last half century, masculinity norms have constricted boys’ achievement and attainment in remarkably persistent, stable ways. To help explain these enduring rigidities, I document steep social and emotional costs for boys who deviate from dominant forms of masculinity. Taken together, these results recast the puzzle of boys’ underperformance and illustrate how machine learning can be used to make new meaning from old data, opening up the binary categories that populate stratification research.

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