Uncovering gaps in women's authorship: A big data analysis in academic surgery
Bibliographic record
Abstract
INTRODUCTION: Women are underrepresented in surgical authorship. Using big data analyses, we aimed to investigate women's representation as first and last authors in surgical publications worldwide and identify underlying predictors. METHODS: We retrieved eligible surgical journals using Scimago Journal & Country Rank 2021. We queried articles indexed in PubMed from selected journals published between January 2018 and April 2022. We used the EDirect tool to extract bibliometric data, including first and last authors' names, primary affiliation country, and publication year. Countries and dependent territories were classified following World Bank income levels and regions. Women's representation was predicted from forenames using the Gender-API software. Citations were included if gender accuracy was ≥80%. RESULTS: We analyzed 210,853 citations containing both first and last authors' forenames, representing 158 countries and 14 territories. Women constituted 23.8% (50,161/210,853) of the first and 14.7% (31,069/210,853) of the last authors. High-income economies had more women as first authors than other income categories (p < 0.001), but fewer women as last authors than upper-middle- and lower-middle-income economies (p < 0.001). The odds of the first author being a woman were more than three times higher when the last author was also a woman (OR 3.21, 95% CI 3.13-3.30) and vice versa (OR 3.25, 95% CI 3.16-3.34) after adjusting for income level and publication year. CONCLUSIONS: Women remain globally underrepresented in surgical authorship. Our findings urge concerted global efforts to overcome identified disparities.
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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 | 0.015 | 0.069 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.027 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".