Is Science Built on the Shoulders of Women? A Study of Gender Differences in Contributorship
Bibliographic record
Abstract
PURPOSE: Women remain underrepresented in the production of scientific literature, and relatively little is known regarding the labor roles played by women in the production of knowledge. This study examined labor roles by gender using contributorship data from science and medical journals published by the Public Library of Science (PLOS), which require each author to indicate their contribution to one or more of the following tasks: (1) analyzed the data, (2) conceived and designed the experiments, (3) contributed reagents/materials/analysis tools, (4) performed the experiments, and (5) wrote the paper. METHOD: The authors analyzed contribution data from more than 85,000 articles published between 2008 and 2013 in PLOS journals with respect to gender using both descriptive and regression analyses. RESULTS: Gender was a significant variable in determining the likelihood of performing a certain task associated with authorship. Women were significantly more likely to be associated with performing experiments, and men were more likely to be associated with all other authorship roles. This holds true controlling for academic age: Although experimentation was associated with academically younger scholars, the gap between male and female contribution to this task remained constant across academic age. Inequalities were observed in the distribution of scientific labor roles. CONCLUSIONS: These disparities have implications for the production of scholarly knowledge, the evaluation of scholars, and the ethical conduct of science. Adopting the practice of identifying contributorship rather than authorship in scientific journals will allow for greater transparency, accountability, and equitable allocation of resources.
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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 | 0.009 | 0.068 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".