Cultural stereotypes such as the idea that men are more suited for paid work and women are more suited for taking care of the home and family, may contribute to gender imbalances in science, technology, engineering and mathematics (STEM) fields, among other undesirable gender disparities. Might these stereotypes be learned from language? Here we examine whether gender stereotypes are reflected in the large-scale distributional structure of natural language semantics. We measure gender associations embedded in the statistics of 25 languages and relate these to data on an international dataset of psychological gender associations (N = 656,636). People’s implicit gender associations are strongly predicted by gender associations encoded in the statistics of the language they speak. These associations are further related to the extent that languages mark gender in occupation terms (for example, ‘waiter’/‘waitress’). Our pattern of findings is consistent with the possibility that linguistic associations shape people’s implicit judgements.

文化定型观念，例如认为男人更适合从事有偿工作，女人更适合照顾家庭和家庭的观念，可能会导致科学，技术，工程和数学（STEM）领域的性别失衡，以及其他不良性别差异。这些刻板印象可以从语言中学习吗？在这里，我们检查性别刻板印象是否反映在自然语言语义学的大规模分布结构中。我们测量25种语言的统计数据中嵌入的性别关联，并将其与国际心理性别关联数据集（N = 656,636）。人们的内隐性别联想是根据他们所说语言的统计数据中编码的性别联想来强烈预测的。这些关联与语言在职业上标记性别的程度（例如“服务员” /“服务员”）进一步相关。我们的发现模式与语言联想塑造人们的内隐判断的可能性是一致的。