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Record W1934702185 · doi:10.1136/bjsports-2015-094962

International Olympic Committee consensus statement on youth athletic development

2015· article· en· W1934702185 on OpenAlexaff
Michael F. Bergeron, Margo Mountjoy, Neil Armstrong, Michael Chia, Jean Côté, Carolyn A. Emery, Avery D. Faigenbaum, Gary Hall, Susi Kriemler, Michel Léglise, Robert M. Malina, Anne Marte Pensgaard, A. Marcos Sánchez, Torbjørn Soligard, Jorunn Sundgot‐Borgen, Willem van Mechelen, Juanita R. Weissensteiner, Lars Engebretsen

Bibliographic record

VenueBritish Journal of Sports Medicine · 2015
Typearticle
Languageen
FieldMedicine
TopicSports injuries and prevention
Canadian institutionsAlberta Children's HospitalMcMaster UniversityQueen's UniversityMcMaster University Medical CentreMarch of Dimes Canada
Fundersnot available
KeywordsAthletesPositive Youth DevelopmentYouth sportsPolitical sciencePublic relationsSports scienceMedical educationPsychologyApplied psychologyMedicinePhysical therapy

Abstract

fetched live from OpenAlex

The health, fitness and other advantages of youth sports participation are well recognised. However, there are considerable challenges for all stakeholders involved-especially youth athletes-in trying to maintain inclusive, sustainable and enjoyable participation and success for all levels of individual athletic achievement. In an effort to advance a more unified, evidence-informed approach to youth athlete development, the IOC critically evaluated the current state of science and practice of youth athlete development and presented recommendations for developing healthy, resilient and capable youth athletes, while providing opportunities for all levels of sport participation and success. The IOC further challenges all youth and other sport governing bodies to embrace and implement these recommended guiding principles.

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)0.052
metaresearch head score (Gemma)0.048
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: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.052
Threshold uncertainty score0.278

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0520.048
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0030.005
Bibliometrics0.0070.005
Science and technology studies0.0020.003
Scholarly communication0.0050.002
Open science0.0090.006
Research integrity0.0120.013
Insufficient payload (model declined to judge)0.0110.009

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 head0.040
GPT teacher head0.298
Teacher spread0.258 · 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
GenreOther

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

Citations870
Published2015
Admission routes1
Has abstractyes

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