Economic geography of innovation: The effect of gender-related aspects of co-inventor networks on country and regional innovation
Bibliographic record
Abstract
This paper focuses on the analysis of the effects of inventor networks on country and regional innovation. We use data from an OECD inventor database that spans more than forty years to build collaboration networks in which the network nodes are countries and regions, and linkages are patents produced by inventors from different regions and countries. We first investigate the network that includes all inventors and then analyze the network focusing on women inventors. We argue that both country and regional-level network centrality positively affect country and regional innovation (with stronger effects at the country level), and centrality in collaborations that involve women has an additional positive impact. We also find that women inventors' share in the pool of inventors is positively associated with innovation quality both at the county and regional levels. Furthermore, our findings indicate that in the network of women inventors, countries and regions that are in cohesive clusters (formed by repeated interactions between interconnected actors) show stronger innovation performance. Our study also highlights important nuances between country-level and region-level effects.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".