Doping controls and the ‘Mature Minor’ elite athlete: towards clarification?
Bibliographic record
Abstract
Doping control is an integral part of participation in sport. It aims to protect the health of athletes and to preserve the integrity and intrinsic values associated with elite sport. The World-Anti-Doping Code applies to all participating athletes, irrespective of their legal capacity and ability to provide informed consent. As such, anti-doping rule violations and the strict liability standard apply to both minor and adult athletes. Under the current Code, minors are defined as any athlete under the age of 18, and so, their vulnerable status may not be fully considered. Participation in sport (including doping control testing) is a unilateral choice – an athlete can accept or refuse to abide by the Code – if refused, the right to compete is forfeited. This article aims to explore the need for further clarification on the new categorisation of ‘mature minor’ elite athletes in the Code. We begin by providing an overview of current doping control testing procedures and the specific issues regarding consent to sample collection under the Code’s strict liability approach with its associated sanctions. We then examine the rights of minors under the Code within the broader international legal context. We conclude with a reflection on how the notion of mature minor has been addressed elsewhere, and how this can further inform ongoing revisions of the Code. These include greater flexibility in the recognition of both contextual and certain statutory criteria.
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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 | 0.049 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.055 |
| Scholarly communication | 0.017 | 0.023 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.020 | 0.028 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".