Re-thinking Women's Sport Research: Looking in the Mirror and Reflecting Forward
Bibliographic record
Abstract
Despite decades of research and advocacy-women's professional sports continue to be considered second class to men's sports. The goal of this paper is to rethink how we state, present, and solve problems in women's sport. To affect true change, the wisdom of a broad stakeholder group was embraced such that varied perspectives could be considered. A three-question survey was developed to examine what key constituents believe is working in women's sports, what they believe the salient challenges are for women's sport, and how they would prioritize the next steps forward in the post-pandemic sport landscape. Results indicated siloed differences of opinion based upon the age and role of the stakeholder in the women's sport ecosystem. We discuss the implications and offer recommendations as to how we as scholars might recalibrate our approach to women's sport scholarship to maximize the impact of our research and affect change.
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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 | 0.075 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.015 | 0.053 |
| Scholarly communication | 0.026 | 0.023 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".