The International Olympic Committee framework on fairness, inclusion and nondiscrimination on the basis of gender identity and sex variations does not protect fairness for female athletes
Bibliographic record
Abstract
The International Olympic Committee (IOC) recently published a framework on fairness, inclusion, and nondiscrimination on the basis of gender identity and sex variations. Although we appreciate the IOC's recognition of the role of sports science and medicine in policy development, we disagree with the assertion that the IOC framework is consistent with existing scientific and medical evidence and question its recommendations for implementation. Testosterone exposure during male development results in physical differences between male and female bodies; this process underpins male athletic advantage in muscle mass, strength and power, and endurance and aerobic capacity. The IOC's "no presumption of advantage" principle disregards this reality. Studies show that transgender women (male-born individuals who identify as women) with suppressed testosterone retain muscle mass, strength, and other physical advantages compared to females; male performance advantage cannot be eliminated with testosterone suppression. The IOC's concept of "meaningful competition" is flawed because fairness of category does not hinge on closely matched performances. The female category ensures fair competition for female athletes by excluding male advantages. Case-by-case testing for transgender women may lead to stigmatization and cannot be robustly managed in practice. We argue that eligibility criteria for female competition must consider male development rather than relying on current testosterone levels. Female athletes should be recognized as the key stakeholders in the consultation and decision-making processes. We urge the IOC to reevaluate the recommendations of their Framework to include a comprehensive understanding of the biological advantages of male development to ensure fairness and safety in female sports.
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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 | 0.233 | 0.199 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.015 | 0.069 |
| Scholarly communication | 0.025 | 0.015 |
| Open science | 0.014 | 0.019 |
| Research integrity | 0.065 | 0.054 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".