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Record W2909691423 · doi:10.1080/24704067.2018.1561206

Understanding Young Athletes’ Learning at the Youth Olympic Games: A Sport Development Perspective

2019· article· en· W2909691423 on OpenAlexaff
Eric MacIntosh, Milena M. Parent, Diane M. Culver

Bibliographic record

VenueJournal of Global Sport Management · 2019
Typearticle
Languageen
FieldPsychology
TopicSport Psychology and Performance
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsAthletesPerspective (graphical)Context (archaeology)PsychologyPerceptionYouth sportsCompetition (biology)Positive Youth DevelopmentApplied psychologyDevelopmental psychologyPhysical therapyMedicineGeographyComputer science

Abstract

fetched live from OpenAlex

This paper examines young athletes’ experiences, perceptions, and learning derived from participating in the Youth Olympic Games (YOG). We draw from Lillehammer 2016 YOG Games-time field notes, observations, and interviews with 36 young athletes to demonstrate young athletes learning about important aspects of the Olympic Movement through first-hand experience. Young athletes focused on their competition and performance, while benefitting from their interactions with other athletes and with the context, as they engaged in formal and informal learning activities. Findings demonstrate progressive degrees of young athlete learning particularly from a development of sport, but also a development through sport perspective. Our study contributes to the examination of Games-time activities, logistics, and processes experienced by the athlete who benefits from a sport (athletic) and social (lifelong) development perspective.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.004
Scholarly communication0.0040.003
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.041
GPT teacher head0.303
Teacher spread0.262 · 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 designQualitative
Domainnot available
GenreEmpirical

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

Citations25
Published2019
Admission routes1
Has abstractyes

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