The robustness of the win–win effect
Bibliographic record
Abstract
We demonstrate that positive relationships between measures of national gender equality and Olympic medal wins are robust across a variety of appropriate statistical approaches to analyzing cross-national data. First demonstrated by Berdahl, Uhlmann, and Bai (2015), who controlled for GDP, population, latitude, and income inequality , we show that relationships between gender equality and medal wins remain positive when controlling for GDP per capita, consistently log-transforming positively skewed variables, and fully analyzing all four gender gap subindexes. The Win–Win effect is most robust for gender equality in education and earnings. Controlling for arbitrarily-defined world regions (“Anglo-Saxon countries” vs. “Africa”) is inappropriate, as such groupings are based on folk stereotypes, not objective scientific criteria, and risks masking meaningful differences between countries. There is, however, often more than one right way to analyze a dataset; we discuss how this can be addressed by crowdsourcing the analysis of complex datasets prior to publication.
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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 | 0.052 | 0.272 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.030 | 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".