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Autor/in | Mittleman, Joel |
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Titel | Intersecting the Academic Gender Gap: The Education of Lesbian, Gay, and Bisexual America. Gefälligkeitsübersetzung: Die Überschneidung des akademischen Geschlechtergefälles: Die Bildung der lesbischen, schwulen und bisexuellen Amerikaner. |
Quelle | In: American sociological review, 87 (2022) 2, S. 303-335Infoseite zur Zeitschrift
PDF als Volltext |
Sprache | englisch |
Dokumenttyp | online; gedruckt; Zeitschriftenaufsatz |
ISSN | 0003-1224; 1939-8271 |
DOI | 10.1177/00031224221075776 |
Schlagwörter | Bildungszugang; Identität; Junge; Frau; Bildungsabschluss; Schulerfolg; Ungleichheit; Homosexualität; Sexualität; Hochschulbildung; Promotion; Studienerfolg; Master-Studiengang; Bachelor-Studiengang; Quote; Hochschulabsolvent; Minderheit; Mann; Mädchen; USA |
Abstract | "Although gender is central to contemporary accounts of educational stratification, sexuality has been largely invisible as a population-level axis of academic inequality. Taking advantage of major recent data expansions, the current study establishes sexuality as a core dimension of educational stratification in the United States. First, I analyze lesbian, gay, and bisexual (LGB) adults' college completion rates: overall, by race/ethnicity, and by birth cohort. Then, using new data from the High School Longitudinal Survey of 2009, I analyze LGB students' performance on a full range of achievement and attainment measures. Across analyses, I reveal two demographic facts. First, women's rising academic advantages are largely confined to straight women: although lesbian women historically outpaced straight women, in contemporary cohorts, lesbian and bisexual women face significant academic disadvantages. Second, boys' well-documented underperformance obscures one group with remarkably high levels of school success: gay boys. Given these facts, I propose that marginalization from hegemonic gender norms has important-but asymmetric-impacts on men's and women's academic success. To illustrate this point, I apply what I call a 'gender predictive' approach, using supervised machine learning methods to uncover patterns of inequality otherwise obscured by the binary sex/gender measures typically available in population research." (Author's abstract, IAB-Doku). |
Erfasst von | Institut für Arbeitsmarkt- und Berufsforschung, Nürnberg |
Update | 2022/3 |