Stereotypes are associations between social groups and semantic attributes that are widely shared within societies. The spoken and written language of a society affords a unique way to measure the magnitude and prevalence of these widely shared collective representations. Here, we used word embeddings to systematically quantify gender stereotypes in language corpora that are unprecedented in size (65+ million words) and scope (child and adult conversations, books, movies, TV). Across corpora, gender stereotypes emerged consistently and robustly for both theoretically selected stereotypes (e.g., work-home) and comprehensive lists of more than 600 personality traits and more than 300 occupations. Despite underlying differences across language corpora (e.g., time periods, formats, age groups), results revealed the pervasiveness of gender stereotypes in every corpus. Using gender stereotypes as the focal issue, we unite 19th-century theories of collective representations and 21st-century evidence on implicit social cognition to understand the subtle yet persistent presence of collective representations in language.

中文翻译：

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自然语言中的性别刻板印象：词嵌入在超过 6500 万词的儿童和成人语言语料库中显示出强大的一致性

刻板印象是社会群体和社会内部广泛共享的语义属性之间的关联。一个社会的口头和书面语言提供了一种独特的方式来衡量这些广泛共享的集体表征的规模和普遍性。在这里，我们使用词嵌入来系统地量化语言语料库中的性别刻板印象，其规模（65+ 百万个单词）和范围（儿童和成人对话、书籍、电影、电视）都是前所未有的。在整个语料库中，无论是理论上选择的刻板印象（例如，在家工作）还是包含 600 多种个性特征和 300 多种职业的综合列表，性别刻板印象都一致且强烈地出现。尽管语言语料库之间存在潜在差异（例如，时间段、格式、年龄组），结果表明，在每个语料库中都普遍存在性别刻板印象。使用性别刻板印象作为焦点问题，我们将 19 世纪的集体表征理论和 21 世纪关于内隐社会认知的证据结合起来，以理解语言中集体表征的微妙而持久的存在。