Gender bias in research teams and the underrepresentation of women in science
Bibliographic record
Abstract
Abstract Why are females still underrepresented in science? The social factors that affect career choices and trajectories are thought to be important but are poorly understood. We analyzed author gender in a sample of >61,000 scientific articles in the biological sciences to evaluate the factors that shape the formation of research teams. We find that authorship teams are more gender-assorted than expected by chance, with excess homotypic assortment accounting for up to 7% of published articles. One possible mechanism that could explain gender assortment and broader patterns of female representation is that women may focus on different research topics than men (i.e., the “topic preference” hypothesis). An alternative hypothesis is that researchers may consciously or unconsciously prefer to work within same-gender teams (the “gender homophily” hypothesis). Using network analysis, we find no evidence to support the topic preference hypothesis, because the topics of female-authored articles are no more similar to each other than expected within the broader research landscape. Instead, consistent with a model of moderate gender homophily, we find that the prevalence of matched-gender teams increases as a discipline moves towards gender parity. This can occur because latent preferences are more easily fulfilled in a gender-diverse environment. Finally, we show that female authors pay a substantial citation cost to work in gender-matched teams. Notably, the prevalence of homotypic assortment is predicted to increase in the future if more disciplines shift towards gender parity. These data indicate that social preferences can have important downstream consequences for the retention of women and other underrepresented groups in science.
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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 | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".