Science and Sex Testing: The Beginnings of a Female Testing Discourse
Bibliographic record
Abstract
In the 1960s, the International Olympic Committee (IOC) sanctioned testing to verify the sex of elite female athletes. Sex tests, as they were called, did not extend to male athletes, and they have tended to rely on appearance and performance alone. Now measuring testosterone levels, the Eligibility Regulations for the Female Classification scrutinizes female athletes far more than male athletes. This dissertation contributes to the sex testing literature by investigating three under-explored avenues: the history of the sex testing sports medical literature, a medical discourse analysis of IOC documents based on the implementation of sex testing, and a critical feminist analysis of the 2019 hearing of runner Mokgadi Caster Semenya.\nData collection comes from a range of sources, including the IOC’s archives, medical journals, IOC Medical Committee correspondence from 1950-1999, current regulations for hyperandrogenism in the IAAF, and the Court of Arbitration of Sport (CAS) hearing Mokgadi Caster Semenya & ASA v IAAF (2019). This dissertation introduces a discourse called ‘female testing,’ highlighting the IOC’s continued history of testing only female athletes for sex. This critical feminist analysis questions the role of the IOC and the IOC medical commission’s science in determining sex-based testing. This dissertation recommends more critical oversight into the relationship between sport science and ethics, and a more pragmatic approach to addressing female testing. Female tests in sport go far beyond what ordinary people are familiar with regarding their biological makeup. The tests currently in place leave some athletes in the women’s category at a disadvantage, including women, women of colour, trans folks, queer-identifying folks, and women from non-Western nations.
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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 | 0.021 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.025 | 0.111 |
| Scholarly communication | 0.016 | 0.013 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.006 | 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".