Avoiding bias when inferring race using name-based approaches.

Racial disparity in academia is a widely acknowledged problem. The quantitative understanding of racial-based systemic inequalities is an important step towards a more equitable research system. However, because of the lack of robust information on authors' race, few large-scale analyses have been performed on this topic. Algorithmic approaches offer one solution, using known information about authors, such as their names, to infer their perceived race. As with any other algorithm, the process of racial inference can generate biases if it is not carefully considered. The goal of this article is to assess the extent to which algorithmic bias is introduced using different approaches for name-based racial inference. We use information from the U.S. Census and mortgage applications to infer the race of U.S. affiliated authors in the Web of Science. We estimate the effects of using given and family names, thresholds or continuous distributions, and imputation. Our results demonstrate that the validity of name-based inference varies by race/ethnicity and that threshold approaches underestimate Black authors and overestimate White authors. We conclude with recommendations to avoid potential biases. This article lays the foundation for more systematic and less-biased investigations into racial disparities in science.

Intersectional inequalities in science.

The US scientific workforce is primarily composed of White men. Studies have demonstrated the systemic barriers preventing women and other minoritized populations from gaining entry to science; few, however, have taken an intersectional perspective and examined the consequences of these inequalities on scientific knowledge. We provide a large-scale bibliometric analysis of the relationship between intersectional identities, topics, and scientific impact. We find homophily between identities and topic, suggesting a relationship between diversity in the scientific workforce and expansion of the knowledge base. However, topic selection comes at a cost to minoritized individuals for whom we observe both between- and within-topic citation disadvantages. To enhance the robustness of science, research organizations should provide adequate resources to historically underfunded research areas while simultaneously providing access for minoritized individuals into high-prestige networks and topics.

Preparation of Underrepresented Males for Scientific Careers: A Study of the Dr. John H. Hopps Jr. Defense Research Scholars Program at Morehouse College.

Equal representation within higher education science, technology, engineering, and mathematics (STEM) fields and the STEM workforce in the United States across demographically diverse populations is a long-standing challenge. This study uses two-to-one nearest-neighbor matched-comparison group design to examine academic achievement, pursuit of graduate science degree, and classification of graduate institution attended by students participating in the Hopps Scholars Program (Hopps) at Morehouse College. Hopps is a highly structured enrichment program aimed at increasing participation of black males in STEM fields. Morehouse institutional records, Hopps Program records, and National Student Clearinghouse data were used to examine differences between Hopps and non-Hopps STEM graduates of Morehouse. Two-way sample t tests and chi-square tests revealed significant differences in academic achievement, likelihood of STEM degree pursuit, and the classification of graduate institutions attended by Hopps versus non-Hopps students. Hopps Scholars were significantly more likely than non-Hopps STEM graduates both to pursue STEM doctoral degrees and to attend doctoral-granting institutions with higher research activity. The Hopps Program's approach to training black male students for scientific careers is a model of success for other programs committed to increasing the number of black males pursuing advanced degrees in STEM.