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.

尽管性别是当代教育分层的核心，但作为学术不平等的人口水平轴，性在很大程度上是无形的。利用最近的主要数据扩展，当前的研究将性行为确立为美国教育分层的核心维度。首先，我分析了女同性恋、男同性恋和双性恋 (LGB) 成年人的大学毕业率：总体而言，按种族/民族和出生队列划分。然后，使用来自 2009 年高中纵向调查的新数据，我分析了 LGB 学生在全方位成就和成就方面的表现。通过分析，我揭示了两个人口统计事实。首先，女性不断上升的学术优势主要局限于异性恋女性：虽然女同性恋女性在历史上超过异性恋女性，但在当代群体中，女同性恋和双性恋妇女在学业上面临重大劣势。其次，有据可查的男孩表现不佳掩盖了一个在学校取得非常高水平的成功的群体：同性恋男孩。鉴于这些事实，我认为霸权性别规范的边缘化对男性和女性的学业成功具有重要但不对称的影响。为了说明这一点，我应用了我称之为“性别预测”的方法，使用监督机器学习方法来揭示不平等的模式，否则人口研究中通常可用的二元性别/性别衡量标准会掩盖这种模式。我认为霸权性别规范的边缘化对男性和女性的学业成功具有重要但不对称的影响。为了说明这一点，我应用了我称之为“性别预测”的方法，使用监督机器学习方法来揭示不平等的模式，否则人口研究中通常可用的二元性别/性别衡量标准会掩盖这种模式。我认为霸权性别规范的边缘化对男性和女性的学业成功具有重要但不对称的影响。为了说明这一点，我应用了我称之为“性别预测”的方法，使用监督机器学习方法来揭示不平等的模式，否则人口研究中通常可用的二元性别/性别衡量标准会掩盖这种模式。