Bibliographic record
Abstract
We analyze more than 70 million scientific articles to characterize the gender dynamics of commercializing science.The double-digit gender gap we report is explained neither by the quality of the science nor its ex-ante commercial potential, and is widest among papers with female last authors (i.e., lab heads) when publishing high-quality science.Using Pitchbook database, we show that when authors self-commercialize scientific discoveries via new ventures, no gap appears, raising the question of whether incumbent firms are unaware of-or ignorescientific contributions by women.A natural experiment based on the Obama administration's staggered introduction of open-access requirements for federally-funded research reveals that although easier access to scientific articles might facilitate commercialization, this benefit accrues primarily to male authors.Articles written with more "boastful" language are commercialized more often, and female scientists generally boast less, but even when they do their discoveries are commercialized no more often.We also observe gender homophily between scientific authors and commercializing inventors, the majority of whom are male.We conclude with the potential welfare effects of the gender gap: the disparity is more pronounced for higher-quality discoveries, as indicated by academic and patent citations or by predicted probabilities of commercialization derived from deep-learning algorithms.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 0.011 |
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".