Race and gender stereotyping in evaluative language

评价性语言中的种族和性别陈规定型观念

• 批准号: 1349043
• 负责人: Monica Biernat
• 金额: $ 12.5万
• 依托单位: University of Kansas Center for Research Inc
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-08-01 至 2017-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1349043&HistoricalAwards=false
• 关键词: Race; gender; stereotyping; evaluative; language

In everyday life, we often are asked to provide assessments or evaluations of others' abilities. Stereotypes can subtly shape these evaluations and judgments, even among those who view themselves as non-prejudiced. This can be very consequential in certain contexts; for example, hiring and admissions decisions can be based in part on the evaluative language used in letters of recommendation, and the language used may be influenced by gender and racial stereotypes. Further, audiences may also interpret this language with reference to those negative stereotypes about women and ethnic minorities. The proposed research addresses how evaluative language content varies depending on the gender and race of the person being described, and how in turn others interpret and use that content. This research has broad implications for understanding bias in real world, evaluative settings. Further, this research will increase our understanding of barriers that women and members of underrepresented groups face when considering educational and career opportunities in STEM (science, technology, engineering and mathematics) fields. Understanding how and when stereotyping can occur in evaluative contexts is a critical step towards reducing bias, prejudice, and discrimination. The proposed research predicts that stereotypes activate different standards of judgments for members of different groups; therefore, evaluations (adjectives) mean different things depending on the person described. For example, in a masculine work domain where women are stereotyped as less competent, "good" for a woman may mean something objectively less good than "good" for a man. Dr. Monica Biernat, University of Kansas, will examine actual letters of recommendation written for male and female, racial majority and minority applicants to a variety of graduate programs at a public university. These letters will be coded for content including positivity, complexity, and use of ability versus effort terms, to examine differences based on gender and race across disciplines that vary in their gender/race representation. The research will address whether these content features predict admission decisions. The findings of this research could be applied to a broad array of real-life settings where evaluations occur.

在日常生活中，我们经常被要求对他人的能力进行评估或评价。刻板印象可以微妙地塑造这些评价和判断，即使是那些认为自己没有偏见的人。在某些情况下，这可能是非常重要的;例如，招聘和录取决定可能部分基于推荐信中使用的评价性语言，而使用的语言可能受到性别和种族陈规定型观念的影响。此外，观众也可能根据对妇女和少数民族的负面定型观念来解释这种语言。拟议的研究解决了评价性语言内容如何根据被描述者的性别和种族而变化，以及其他人如何反过来解释和使用这些内容。这项研究对于理解真实的世界、评估环境中的偏见具有广泛的影响。此外，这项研究将增加我们对妇女和代表性不足的群体成员在考虑STEM（科学，技术，工程和数学）领域的教育和职业机会时所面临的障碍的理解。了解在评价背景下如何以及何时会出现陈规定型观念，是减少偏见、偏见和歧视的关键一步。拟议的研究预测，刻板印象激活不同群体成员的不同判断标准;因此，评价（形容词）意味着不同的东西取决于所描述的人。例如，在一个男性工作领域，女性被定型为能力较低，“好”的女人可能意味着客观上不如“好”的男人。莫妮卡Biernat博士，堪萨斯大学，将检查实际的推荐信写的男性和女性，种族多数和少数民族申请人的各种研究生课程在公立大学。将对这些信件的内容进行编码，包括积极性、复杂性以及能力与努力术语的使用，以检查性别/种族代表性不同的学科中基于性别和种族的差异。该研究将解决这些内容特征是否能预测录取决定。这项研究的结果可以应用于广泛的现实生活中的评估发生的设置。