Stereotypes in High-Stakes Decisions: Evidence from U.S. Circuit Courts

Abstract

Do gender attitudes influence interactions with female judges in U.S. Circuit Courts? In this paper, we propose a novel judge-specific measure of gender attitudes based on use of genderstereotyped language in the judge's authored opinions. Exploiting quasi-random assignment of judges to cases and conditioning on judges' characteristics, we validate the measure showing that slanted judges vote more conservatively in gender-related cases. Slant influences interactions with female colleagues: slanted judges are more likely to reverse lower-court decisions if the lower-court judge is a woman than a man, are less likely to assign opinions to female judges, and cite fewer female-authored opinions.

Content Analysis of Textbooks via Natural Language Processing: Findings on Gender, Race, and Ethnicity in Texas U.S. History Textbooks

“…A number of recent studies outside education have used NLP methods to study the reflection of gender and other social variables in text: Fast et al (2016) look at gender stereotypes in online fiction; Hoyle et al (2019) measured the association of adjectives and verbs with different genders in a million digitized books; Garg et al (2018) quantified a century of gender and ethnic stereotypes using word representations learned from books, newspapers, and other texts; and Ash et al (2020) examine the role of gender slant in judicial behavior using text written by judges. We build on this line of work examining depictions of social groups in texts (see also Field et al, 2019;Joseph et al, 2017;Ornaghi et al, 2019), extending NLP methods to textbooks.…”

The impact of the #MeToo movement on language at court A text-based causal inference approach

“…Other studies use text as treatment or outcome. [24] analyze how a judge's score on an indicator of gender-stereotyped language affects their decisions on women's rights' issues. [25] assess how wording in tweets affects the number of re-tweets.…”

A Tool Kit for Relation Induction in Text Analysis

“…If the cosine is zero-i.e., the angle is perpendicular/orthogonal-they are equidistant to both man and woman. Therefore, this method has been used extensively to measure the extent target terms are biased toward man or woman (Bolukbasi et al 2016a, Caliskan et al 2017, Ornaghi et al 2019, Jones et al 2020, Arseniev-Koehler and Foster 2022. 15 To increase the accuracy of this direction, we can find several gendered vectors corresponding to man on the one hand (e.g., # » gentlemen, # » boys) and woman on the other (e.g., # » ladies, # » girls), and then summarize the vector offset in one of three ways.…”