Race and gender stereotyping in evaluative language

在日常生活中，我们经常被要求提供对他人能力的评估或评估。刻板印象可以微妙地塑造这些评价和判断，即使是在那些认为自己没有偏见的人中也是如此。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，而使用的语言可能会受到性别和种族陈规定型观念的影响。此外，观众还可以参照对妇女和少数族裔的负面成见来解释这种语言。这项拟议的研究解决了评价性语言内容如何根据被描述人的性别和种族而变化，以及其他人如何解释和使用这些内容。这项研究对理解现实世界中的偏见、评价环境具有广泛的意义。此外，这项研究将增加我们对妇女和代表性不足群体成员在考虑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。了解如何以及何时在评价环境中发生陈规定型观念，是减少偏见、偏见和歧视的关键一步。拟议的研究预测，刻板印象会为不同群体的成员激活不同的判断标准；因此，评估(形容词)的含义取决于所描述的人。例如，在男性工作领域，女性被刻板印象为能力较差，女性的“好”可能意味着客观上不如男性的“好”。堪萨斯大学的莫妮卡·比尔纳特博士将审查为一所公立大学各种研究生项目的男性和女性、多数族裔和少数族裔申请者写的实际推荐信。这些字母将根据内容进行编码，包括积极性、复杂性和能力与努力术语的使用，以检查基于性别和种族的不同学科之间的差异，这些学科在性别/种族代表方面存在差异。这项研究将探讨这些内容特征是否能预测录取决定。这项研究的发现可以应用于进行评估的广泛的现实生活环境中。

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.