A Triple Penalty for Women Entrepreneurs? A University and Field Experiment of STEM Pitches Using AI
Bibliographic record
Abstract
Research on resource acquisition for women entrepreneurs argues that there is a “double penalty” faced by women when they develop products and seek funding: a structural one associated with being a woman (versus a man), and behavioral one, for acting in ways consistent with stereotypes of women versus men seeking funding. We investigate the possibility of a third penalty, one associated with AI use, a novel technology for augmenting entrepreneurial knowledge and activities but also of debated legitimacy. We do so in the context of science, technology, engineering and management (STEM) product development and commercialization. In that context novelty is prized and female entrepreneurship encouraged, particularly by government-related funding agencies, making it a conservative test of the triple penalty possibility. In our university based experiment, we found some evidence of all three penalties for women STEM entrepreneurs, and of evaluator effects. However, the woman STEM entrepreneur sometimes earned more - or were penalized less - than the male. In the field experiment with the government funding unit for the pitched products, we found that AI use itself was punished for both men and women science team members pitching with it for seed-funding, but being a woman and/or being evaluated by man as a panel member did not make a large direct difference, rather the products’ characteristics did. Our research contributes to research on women STEM entrepreneur resource acquisition in the technology sector as well as to work on pitching. Our work also has secondary contributions to burgeoning research on the use of generative AI as an entrepreneurial tool.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".