Bibliographic record
Abstract
U.S. women’s soccer team, the champion of Women’s World Cup in Canada 2015, made $2 million, which is a tiny fraction of the $35 million the German men’s team made for winning the Men’s World Cup in Brazil 2014. Soccer is not the only professional sport where there is a wide gender gap in prize money. At the U.S. Womens Open in 2015, prize money for the champion female golfer is $810,000 while $1.8 million was given to the male champion at the men’s U.S. Open in the same year. However, some professional sports have reached parity. At the 2015 Wimbledon, both the winner of the men’s final and the women’s final will earn about $2.9 million as a prize. Since 2007 when Wimbledon finally joined prize parity, four tennis Grand Slam tournaments have provided equal prize money to men and women players. This paper will analyze opinions and theoretical background on prize parity across players’ gender. We will also investigate the existence and magnitude of prize gap across genders in professional sports in Eastern Asia. Then the determinants of prize gender gap in professional sports held at that region will be examined. In particular, the effect of cultural difference in women’s role in Korea, China, and Japan on gender gap in prize structure will be the core contribution of this paper.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".