从嵌入式中提取交叉刻板印象:开发和验证灵活交叉刻板印象提取程序
Tessa E S Charlesworth1, Kshitish Ghate2, Aylin Caliskan3
1Kellogg School of Management, Northwestern University, Evanston, IL 60208, USA.
PNAS nexus
|March 20, 2024
概括
本研究引入了灵活交叉刻板印象提取 (FISE) 来分析性别,种族和阶级等社会身份如何在日常语言中交叉. 它揭示了占主导地位的刻板印象和相关的特征,突出了大型文本体中的偏见.
科学领域:
- 计算社会科学 计算社会科学
- 自然语言处理自然语言处理.
- 社会语言学 社会语言学
背景情况:
- 性别,种族和阶级等社会认同不是孤立地经历的,而是相互交叉的.
- 了解这些交叉点如何以大规模的语言表现,对于社会科学研究至关重要.
- 现有的方法很难在庞大的文本数据中有效量化交叉刻板印象.
研究的目的:
- 开发和验证一种新的计算方法,灵活的交叉刻板印象提取 (FISE),用于分析日常语言中的交叉刻板印象.
- 量化与各种性别,种族和阶级的交叉点相关的刻板印象的频率和性质.
- 揭示关于社会群体身份的语言使用中的偏见和模式.
主要方法:
- 灵活交叉刻板印象提取 (FISE) 程序的开发.
- 将FISE应用于词嵌入 (GloVe,BERT) 训练在大型英语互联网文本大体上.
- 根据地面真相验证职业人口统计学和特征形容词关联的分析.
主要成果:
- FISE成功地与职业人口统计学保持一致,验证了其方法.
- 表现出显著的人类中心主义和种族中心主义,其中"白人+男性"与59%的特征相关,而"黑人+女性"与5%相关.
- 交叉群体,特别是那些涉及"富人"的群体,与更频繁的,积极的,温暖的,有能力的和主导的特征有关.
结论:
- FISE提供了一种透明和有效的工具,用于量化大型文本体中的交叉刻板印象.
- 这些发现揭示了普遍的偏见,并突出了基于交叉的社会身份的特质的差异性归因.
- 这项工作为未来研究交叉身份及其在语言中的新兴特性建立了基础设施.
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