The use of doping control data to administer sex-based eligibility regulations: an analysis of how the World Anti-Doping Agency and international sport federations violate data protection laws
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract The World Anti-Doping Agency (WADA)’s World Anti-Doping Code (WADC) provides that anti-doping organizations, such as international sport federations (IFs), may use data from a doping control test to monitor compliance with sex-based eligibility regulations that regulate the serum testosterone levels of transgender and intersex athletes. This contemplated use of doping control data has been incorporated into the regulations of several IFs and is facilitated by WADA’s Anti-Doping Administration Management System (ADAMS)—a web-based database managed by WADA in Canada that contains analytical results from doping control tests and is accessible by anti-doping organizations. WADA’s collection, use and disclosure of personal information through ADAMS is subject to Canada’s Personal Information Protection and Electronic Documents Act (PIPEDA). This paper examines WADA’s non-compliance with PIPEDA when it discloses doping control data in ADAMS to an IF for the purpose of the IF’s administration of sex-based eligibility regulations, and how a complaint about WADA’s data disclosures might be handled by Canada’s Privacy Commissioner. The paper also examines the application of the European Union’s General Data Protection Regulation to IFs that seek to use doping control data stored in or outside of ADAMS to administer sex-based eligibility regulations. The paper concludes with the perspective that data protection laws can be used to challenge the implementation of sex-based eligibility regulations, alongside other human rights-based legal strategies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it