A Study on Men's AND Women's Surface Preferences From the Same Country
Bibliographic record
Abstract
There are some pretty noticeable differences. Canada and Portugal, for example, rate as solid hard-court countries on the men's side but mild clay-court countries on the women's side. And it's not like there are single players like Milos Raonic skewing the average; players like VasekPospisil, Frank Dancevic, and Erik Chvojka have Raonic's same surface numbers. And there isn't a single Canadian men's player that has a significant clay preference. The Canadian women, however, have a more mixed draw of surface preferences. For every hardcourt specialist like Stephanie Dubois, there's another clay-court specialist like Sharon Fichman. The distribution between hard and clay preferences skews about even. This brings up the million dollar question that's bugging me: Should the men's and women's surface maps converge to the same over time? Your answer roughly reflects two equally plausible views. If you say yes, that says surface preference is mostly determined by your home country's common courts and any differences are transient and due to small sample size. This is supported by a reasonable number of countries that are colored the same on the men's and women's maps. If you say no, that says you can have meaningful differences between men's and women's surface preferences from the same country. The most fitting explanation would be some sort of selection bias with regards to what kinds of players are more likely to succeed at an early age.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".