Intersectional inequalities of representation and research topics in science
Bibliographic record
Abstract
The scientific workforce of the United States is mostly composed of white men. Barriers to entry and participation have been well-studied. However, few have adopted an intersectional perspective to examine the consequences of these inequalities in scientific knowledge. In this work, a large-scale bibliometric analysis is provided on the relationship between intersectional identities, research topics, and scientific impact. Using the Web of Science bibliometric database for US publications between 2008 and 2019, an analysis of over and underrepresentation of different racial and gender identities was conducted, as well as their distribution across different disciplines. The findings reveal a marked underrepresentation of women and marginalized racial groups (Latinxs and Black individuals), with women from these identities being the most affected. Additionally, an asymmetric distribution of racial and gender identities across disciplines is observed, with a notable underrepresentation of women in areas such as Physics, Mathematics, and Engineering. Subsequently, the analysis is deepened by modeling research topics in the Social Sciences and Health. The results show that women tend to publish more in topics such as education, nursing, and gender-based violence, while Black authors are overrepresented in studies on racial discrimination and Latinxs in migration and topics related to Latinx bodies. This distribution in different research topics is in turn related to the academic impact each topic entails. Topics where privileged groups are overrepresented are also the most cited on average, while marginalized groups tend to receive fewer citations in all topics.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.023 | 0.047 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".