Gendered Innovation Landscapes: Entrepreneurial Patenting in Male- and Female-Dominated Industries
Bibliographic record
Abstract
Patents signal innovation quality and help entrepreneurs attract resources, yet gender disparities in patenting persist. While research has attributed the patenting gender gap to differences in education, networks, access to support mechanisms, and patenting’s characterization as a male-dominated activity, we have a limited understanding of how entrepreneurs patent across different industry contexts. Using a sample of 4,743 early-stage current entrepreneurs from the U.S. 2022-2023 Entrepreneurship in the Population (EPOP) Survey Project dataset and categorizing industries by the gender composition of the U.S. workforce, we find that men hold a higher share of patents in both female-dominated (56.91%) and male-dominated industries (61.59%). In female-dominated industries, women leverage internal networks and ecosystem support, while men benefit from external networks at higher education levels and out-patent women. In male-dominated industries, while ecosystem support and networks correspond with a higher likelihood for both genders, men maintain their patenting lead. Thus, while men successfully navigate incongruent contexts, women consistently struggle across contexts, highlighting how structural barriers, including masculine-typing of innovation activities, perpetuate uneven outcomes. We open avenues for research and offer insights for equitable innovation outcomes.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.000 |
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".