Gender-related aspects of invention networks: A firm-level analysis
Bibliographic record
Abstract
This paper integrates insights from the literature on invention networks, gender, and the sociological literature to analyze differences in how firms participate in man-led and woman-led invention networks. We contribute to the current debate on whether clustering or boundary-spanning network properties are more important for invention by introducing gender as an important factor. We empirically test our hypotheses on a sample of more than 30,000 firms from around the world over time using OECD REGPAT global patent data. Our findings indicate that different network properties are important for firm invention in woman-led and man-led innovation networks. In man-led invention networks, firms strongly benefit from being in a boundary-spanning position and are negatively affected by clustering, whereas in woman-led invention networks, boundary spanning has a less pronounced positive effect, and clustering has a positive rather than negative effect. Our findings have substantial implications for firms and policymakers interested in invention and contribute to the studies of gender and invention networks. • Investigates how gender shapes the impact of network positions on invention outcomes. • Finds that clustering and boundary spanning affect invention differently across team genders. • Shows that cohesion can enhance invention performance depending on team composition. • Constructs innovation networks from global patent data. • Provides guidance for firms aiming to design inclusive, innovation-oriented collaborations.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".