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   Race and gender homophily in collaborations and citationsKozlowski, Diego  ; ; et alScientific Conference (2022, October 09) Detailed reference viewed: 219 (5 UL)Institutional determinants of intersectional inequalities in scienceKozlowski, Diego ; ; et alin BRIDGES BETWEEN DISCIPLINES: GENDER IN STEM AND SOCIAL SCIENCES (2022, September 12) Detailed reference viewed: 104 (6 UL)Automatic Classification of Peer Review RecommendationKozlowski, Diego ; ; et alPoster (2022, September 08) Detailed reference viewed: 38 (2 UL) Applying an Intersectional Lens to Author Composition at Women’s Colleges, Historically Black Colleges and Universities, and Hispanic Serving Institutions in the United StatesKozlowski, Diego ; ; et alScientific Conference (2022, September 07) Detailed reference viewed: 27 (3 UL)Intersectional Inequalities in ScienceKozlowski, Diego ; ; et alin Proceedings of the National Academy of Sciences of the United States of America (2022), 119(2), 2113067119 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 ... [more ▼] 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. [less ▲] Detailed reference viewed: 63 (5 UL)Avoiding bias when inferring race using name-based approachesKozlowski, Diego ; ; et alin 18th INTERNATIONAL CONFERENCE ON SCIENTOMETRICS & INFORMETRICS, 12–15 July 2021KU Leuven, Belgium (2021, July) 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 ... [more ▼] 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, few large-scale analyses have been performed on this topic, mostly because of the lack of robust race-disambiguation algorithms. Identifying author information does not generally include the author’s race. Therefore, an algorithm needs to be employed, using known information about authors, i.e., their names, to infer their perceived race. Nevertheless, as any other algorithm, the process of racial inference can generate biases if it is not carefully considered. When the research is focused on the understanding of racial-based inequalities, such biases undermine the objectives of the investigation and may perpetuate inequities. The goal of this article is to assess the biases introduced by the different approaches used name-based racial inference. We use information from US census and mortgage applications to infer the race of US author names 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 and ethnicity and that threshold approaches underestimate Black authors and overestimate White authors. We conclude with recommendations to avoid potential biases. This article fills an important research gap that will allow more systematic and unbiased studies on racial disparity in science. [less ▲] Detailed reference viewed: 163 (7 UL) |
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