Applying an Intersectional Lens to Author Composition at Women's Colleges, Historically Black Colleges and Universities, and Hispanic Serving Institutions in the United States
Bibliographic record
Abstract
Our analysis reinforces the overrepresentation of White authors across institution types, even within those with missions or threshold-based criteria that primarily serve a non-White student population. The most striking observation from our institutional analysis was the proportional overrepresentation of White and Asian men and women as published authors at HBCUs. This statistic raises questions regarding support needed to facilitate the work of Black academics, and the persistent structural barriers within the higher education industry that lead to the continued underrepresentation of Black women and men in academia. Future research should explore these findings with survey and/or interview data to unpack these patterns; and identify the racialized and gendered, motivational and institutional factors that drive publication rates by race/ethnicity and gender of authors at these institutions over time. Despite the overwhelming majority of White authors, our analysis suggests that HBCUs, HSIs, and women’s colleges represent authors from their respective target populations to a degree that is higher than expected among all authors. However, a comparison with the U.S. population shows an overrepresentation of Asian authors and the analysis by doctoral graduates suggests an overrepresentation of Black men. These analyses suggest that the data are highly sensitive to normalization, with each approach reflecting a different set of inequities to content within the U.S. author population. This study sets a platform for future work on how institutions shape diversification of scientific knowledge within the U.S. context.
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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 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.016 | 0.017 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".