Examining procedural fairness in anti-doping disputes: a comparative empirical analysis
Bibliographic record
Abstract
Abstract While the principles of procedural fairness apply in anti-doping disputes pursuant to Article 8 of the Word Anti-Doping Code, 2021 (the Code), there has been limited research assessing whether due process requirements are applied consistently by national anti-doping tribunals. This paper investigates the extent to which the procedural requirements set out under the Code are followed in practice, with a focus on India, New Zealand and Canada, facilitating comparison between developed and developing jurisdictions. By providing an evidence-based examination of first instance anti-doping procedures, this study confirms existing theories on the overall lack of harmonization in anti-doping procedures. We undertook a frequency analysis on the full-text awards handed down by first instance anti-doping tribunals in the comparative jurisdictions and the findings highlight inconsistent application of timeliness requirements and access to legal representation. Critically, in India, disputes take significantly longer to be resolved than in Canada and New Zealand, while far fewer Indian athletes are represented by legal counsel. In all jurisdictions, athletes who were represented by counsel were more likely to see a reduction in their sanctions. The study provides empirical evidence of systemic issues associated with timeliness and access to justice in anti-doping tribunals across jurisdictions and reinforces the need to focus on capacity building and enforcement of procedural safeguards, especially in developing countries. Practical recommendations include strategies to better achieve compliance and harmonization in protecting the procedural rights of athletes, particularly those athletes affected by the current application of the Code where cultural and socio-economic barriers may exacerbate procedural issues.
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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 | 0.061 | 0.241 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".