Intersectional Analysis of Health Inequalities Research Authorship in the United Kingdom (1970–2023): Towards an Inclusive Scholarship?
Bibliographic record
Abstract
Gender inequalities in authorship have extensively been investigated, yet evidence on ethnic inequalities remains limited, with even fewer studies examining the intersections of the two. Our study aims to identify and measure the magnitude of intersectional (gender-by-ethnicity) inequalities among United Kingdom (U.K.)-affiliated-first authors in health inequalities research (1970-2023), and investigate how ethnic inequalities are distributed between and within gender groups over time. The study focuses on U.K. authorship due to its long health inequalities research tradition. We conducted bibliometric analysis of the health inequalities field using the Scopus database, limiting our analysis to U.K.-affiliated authors. Based on first and family names, four strategies were adopted to identify the authors' gender; the Consumer Data Research Centre's Ethnicity Estimator software was used to identify their ethnicity. Despite a decline in the representation of White male first authors over time, all other intersectional groups-especially Black/British Caribbean and Asian/British Bangladeshi authors-show markedly lower representation overall and consistently, with minimal contributions compared to their White male and female counterparts. Our findings offer a nuanced understanding of how different social groups have contributed to the U.K.'s health inequalities research field over time. Addressing these epistemic injustices is essential to enrich the field and strengthen efforts to tackle health inequalities.
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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 | 0.022 | 0.146 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.041 | 0.098 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".