The <i>W</i> -index: a novel tool to evaluate gender equity in STEM research
Bibliographic record
Abstract
Abstract Gender inequities in scientific research persist, from citation indices to collaboration networks and funding success. Current estimates for achieving equal representation remain in timescales of decades to centuries. To help individuals and groups improve their gender representation in a shorter timeframe, we present the W -index, a simple metric to evaluate gender ratios, and specifically the representation of women, in research collaborations and other aspects of scientific life. Here it is applied to three case studies: internal collaborations derived from six years of research outputs across a university, co-author groups from 60 years of publications across a journal collection, and supervisor-student relationships over 60 years. Despite contrasting sources, these datasets show common features: W -index increases as the proportion of women increases W -index also increases with age or career stage, and women have a consistently higher W -index compared to men. We finish with reflections and recommendations for individuals, organisations, and funding bodies to action positive and timely change towards gender equity.
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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 | 0.013 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.017 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".