Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.003 |
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 it