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Record W2980739978 · doi:10.1080/19406940.2019.1680416

Doping controls and the ‘Mature Minor’ elite athlete: towards clarification?

2019· article· en· W2980739978 on OpenAlexaff
Erika Kleiderman, Rachel Thompson, Pascal Borry, Audrey Boily, Bartha Maria Knoppers

Bibliographic record

VenueInternational Journal of Sport Policy and Politics · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicDoping in Sports
Canadian institutionsMcGill UniversityMcGill Genome Centre
Fundersnot available
KeywordsContext (archaeology)Minor (academic)SanctionsFlexibility (engineering)Statutory lawAthletesLiabilityLawCode (set theory)ElitePsychologyPolitical sciencePublic relationsMedicineComputer sciencePhysical therapyEconomicsPolitics

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.049
metaresearch head score (Gemma)0.079
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.049
Threshold uncertainty score0.259

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0490.079
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0090.055
Scholarly communication0.0170.023
Open science0.0040.010
Research integrity0.0200.028
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.014
GPT teacher head0.332
Teacher spread0.318 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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".

Quick stats

Citations11
Published2019
Admission routes1
Has abstractyes

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Same venueInternational Journal of Sport Policy and PoliticsSame topicDoping in SportsFrench-language works237,207