Methodology Review: A Protocol to Audit the Representation of Female Athletes in Sports Science and Sports Medicine Research
Bibliographic record
Abstract
Female-specific research on sports science and sports medicine (SSSM) fails to mirror the increase in participation and popularity of women's sport. Females have historically been excluded from SSSM research, particularly because their physiological intricacy necessitates more complex study designs, longer research times, and additional costs. Consequently, most SSSM practices are based on research with men, despite potential problems in translation to females due to sexual dimorphism in biological and phenotypical parameters as well as differences in event characteristics (e.g., race distances/durations). Recognition that erroneous extrapolations may hamper the efforts of females to maximize their athletic potential has created an impetus to acknowledge and readdress the sex disparity in SSSM research. To direct the priorities for future research, it is prudent to first develop a comprehensive understanding of the gaps in current knowledge by systematically "auditing" the literature. By conducting audits of the literature to highlight underdeveloped topics or identify potential problems with the quality of research, this information can then be used to expediently direct new research activities. This paper therefore presents a standardized audit methodology to establish the representation of female athletes in subdisciplines of existing SSSM research, including a template for reporting the results of key metrics. This standardized audit process will enable comparisons over time and between research subdisciplines. This working guide provides an important step toward achieving sex equity across SSSM research, with the eventual goal of providing evidence-based recommendations specific to the female athlete.
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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 | 0.211 | 0.239 |
| Meta-epidemiology (narrow) | 0.007 | 0.006 |
| Meta-epidemiology (broad) | 0.012 | 0.008 |
| Bibliometrics | 0.016 | 0.016 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.074 | 0.028 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".