We combined established psychological measures with techniques in machine learning to measure changes in gender stereotypes over the course of the 20th century as expressed in large-scale historical natural language data. Although our analysis replicated robust gender biases previously documented in the literature, we found that the strength of these biases has diminished over time. This appears to be driven by changes in gender biases for stereotypically feminine traits (rather than stereotypically masculine traits) and changes in gender biases for personality-related traits (rather than physical traits). Our results illustrate the dynamic nature of stereotypes and show how recent advances in data science can be used to provide a long-term historical analysis of core psychological variables. In terms of practice, these findings may, albeit cautiously, suggest that women and men can be less constrained by prescriptions of feminine traits. Additional online materials for this article are available on PWQ’s website at 10.1177/0361684320977178

我们将成熟的心理测量方法与机器学习技术相结合，以测量大规模历史自然语言数据中所表达的20世纪性别定型观念的变化。尽管我们的分析复制了以前文献中记录的强烈的性别偏见，但我们发现这些偏见的强度随着时间的推移而减弱。这似乎是由于对定型女性特征（而不是定型男性特征）的性别偏见的变化以及对与人格相关的特征（而非身体特征）的性别偏见的变化所驱动。我们的研究结果说明了刻板印象的动态性质，并说明了如何利用数据科学的最新进展对核心心理变量进行长期历史分析。在实践中，这些发现可能会可在PWQ的网站上找到本文的其他在线材料，网址为10.1177 / 0361684320977178