Bibliographic record
Abstract
Izumi et al. (2023) document the existence of CEO gender homophily in firm-to-firm transactions, where CEOs of the same gender are more likely to trade more than those of the opposite gender, putting female CEOs at a disadvantage in a male-dominated business landscape. In this paper, we examine whether informal networking tools, in particular playing golf as a hobby, mitigate this disadvantage for female CEOs. Using a unique dataset that includes both CEO hobbies and detailed inter-firm networks, we show that playing golf does not benefit female CEOs in finding male business partners, while for male CEOs playing golf is associated with a higher share of trading with male CEOs. This result suggests that women’s participation in traditionally male-dominated socializing activities does not necessarily help them gain access to male business networks. • We explore if golf as a hobby helps female CEOs overcome barriers in networking with men. • We use a unique dataset on CEO hobbies and detailed inter-firm networks in Japan. • Playing golf does not help female CEOs find male business partners. • Male CEOs who play golf have a higher share of business with other male CEOs. • Women’s participation in male-dominated social activities doesn’t always improve access to male networks.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.024 | 0.005 |
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".