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1. Labels vs. Pictures: Treatment-Mode Effects in Experiments About Discrimination.

   https://doi.org/doi:10.1017/pan.2017.36  

   Abrajano, M.; Elmendorf, C.; Quinn, K. (January 2018, Political analysis)

   Does treatment mode matter in studies of the effects of candidate race or ethnicity on voting decisions? The assumption implicit in most such work is that such treatment mode differences are either small and/or theoretically well understood, so that the choice of how to signal the race of a candidate is largely one of convenience. But this assumption remains untested. Using a nationally representative sample of white voting-age citizens and a modified conjoint design, we evaluate whether signaling candidate ethnicity with ethnic labels and names results in different effects than signaling candidate ethnicity with ethnically identifiable photos and names. Our results provide strong evidence that treatment-mode effects are substantively large and statistically significant. Further, these treatment-mode effects are not consistent with extant theoretical accounts. These results highlight the need for additional theoretical and empirical work on race/ethnicity treatment-mode effects. 

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2. A Rose by Any Other Name would not Smell as Sweet: Social Bias in Names Mistranslation

   https://doi.org/10.18653/v1/2023.emnlp-main.239  

   Sandoval, Sandra; Zhao, Jieyu; Carpuat, Marine; Daumé_III, Hal (January 2023, Association for Computational Linguistics)

   We ask the question: Are there widespread disparities in machine translations of names across race/ethnicity, and gender? We hypothesize that the translation quality of names and surrounding context will be lower for names associated with US racial and ethnic minorities due to these systems' tendencies to standardize language to predominant language patterns. We develop a dataset of names that are strongly demographically aligned and propose a translation evaluation procedure based on round-trip translation. We analyze the effect of name demographics on translation quality using generalized linear mixed effects models and find that the ability of translation systems to correctly translate female-associated names is significantly lower than male-associated names. This effect is particularly pronounced for female-associated names that are also associated with racial (Black) and ethnic (Hispanic) minorities. This disparity in translation quality between social groups for something as personal as someone's name has significant implications for people's professional, personal, and cultural identities, self-worth and ease of communication. Our findings suggest that more MT research is needed to improve the translation of names and to provide high-quality service for users regardless of gender, race, and ethnicity. 

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3. Demographic Diversity and Economic Research: Fields of Specialization and Research on Race, Ethnicity, and Inequality

   https://doi.org/10.1257/pandp.20241129  

   Antman, Francisca M; Doran, Kirk B; Qian, Xuechao; Weinberg, Bruce A (May 2024, AEA Papers and Proceedings)

   Using dissertation research topics found in the EconLit database and large-scale algorithmic methods that identify author demographics based on names, we explore the link between race and ethnicity and fields of economic research. We find that underrepresented racial and ethnic minority (URM) researchers are more likely to write dissertations in some unexpected subfields of economics but limited evidence that they are more likely to write dissertations on racial topics once we include basic controls. These descriptive results may be due to limitations in the data, intrinsic motivations, or external constraints. 

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4. The Uneven Later Work Course: Intersectional Gender, Age, Race, and Class Disparities

   https://doi.org/10.1093/geronb/gbab039  

   Moen, Phyllis; Flood, Sarah M; Wang, Janet (March 2021, The Journals of Gerontology: Series B)

   null (Ed.)

   Abstract Objectives Later adult work attachments and exits are in flux, suggesting the need for understanding both the range of contemporary population-level pathways of work and non-work and variations by overlapping social locations. We document patterned continuity and change in monthly work attachments and analyze the intersecting effects of age, gender, education, and race/ethnicity. Methods We capitalize on massive micro-level 16-month panel data from the Current Population Survey (CPS) from 2008 through 2016 to empirically identify patterned pathways of monthly states: working full time, long hours, part time; being self-employed or unemployed; not working because of a disability, due to family care or other reasons, or because one defines oneself as retired. Results Analyses of 346,488 American women and men ages 50 to 75 reveal patterned elasticity in the timing and nature of work attachments in the form of six distinctive pathways. Our intersectional analyses illustrate divergences and disparities: advantages for educated white men, disadvantages for low-educated Black men and women through their early 60s, and intersecting effects of gender, education, and race/ethnicity during the later work course across age groups. We find convergence across social markers by the 70s. Discussion This research highlights the importance of intersectional analysis, recasting the gendered work course in later adulthood into a framework of even greater complexities within mutually shaping categories of race/ethnicity, class, and age. Older Americans experience patterned, uneven pathways around work and non-work. We recommend additional scholarship on the dynamics of constrained and disparate choices unfolding across multiple intersecting social locations. 

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5. Students' Experiences of Unfairness in Graduate Engineering Education

   Bahnson, M.; Satterfield, D.; Wyer, M.; Kirn, A. (January 2021, The Collaborative Network for Engineering and Computing Diversity)

   null (Ed.)

   A pilot inventory to develop measures of bias and discrimination experienced by engineering doctoral students asked if they have been treated unfairly by their primary advisor, secondary advisor, and other faculty. Analyses of pilot data (n = 250) revealed Women, Students of Color, and sexual minorities perceived experiences of unfair treatment in intricate patterns. Post hoc analyses show that Women experience more incidences of unfair treatment than men. Race/ethnicity identity groups report a different number of unfair treatment incidences, with Students of Color generally reporting more experiences than white students. Being a sexual minority contributed to reporting more incidences of unfair treatment. Unfair treatment from faculty significantly predicted students changing and considering changing research labs when controlling for gender, race/ethnicity, and sexuality. Unfair treatment from faculty significantly impacted engineering identity when controlling for gender, race/ethnicity, sexuality, lab changers, and change considerers. Analyses of pilot data demonstrated the negative impact of unfair treatment on students and their development as engineers. 

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