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Record W2603530909 · doi:10.6063/motricidade.6099

Fostering Elite Athlete Development and Recreational Sport Participation: a Successful Club Environment

2017· article· en· W2603530909 on OpenAlexaff
Larissa Rafaela Galatti, Jean Côté, Riller Silva Reverdito, Veronica Allan, Antonio Montero Seoane, Roberto Rodrigues Paes

Bibliographic record

VenueMotricidade · 2017
Typearticle
Languageen
FieldPsychology
TopicSport Psychology and Performance
Canadian institutionsQueen's University
Fundersnot available
KeywordsClubEliteRecreationYouth sportsBasketballPublic relationsPositive Youth DevelopmentPsychologyPolitical scienceApplied psychologyAthletesPhysical therapyDevelopmental psychologyGeographyMedicine

Abstract

fetched live from OpenAlex

The overall aim of this article was to present a positive case study about how a sport club can foster both elite athlete development in parallel with offering a diverse range of sport activities to attract and maintain a greater number of children and youth for continued participation in a long term sport program. To this end, an in-depth case study was conducted of a model Spanish Basketball Club, considered an example of success in achieving consistent level of performance and high rates of participation among their youth. Data were collected from in-depth interviews with administrators, setting observation, and analysis of current and archived club documents. The results show that the club has created changes over the years that have led to a clear organizational structure with a philosophy that connects its youth development teams and elite teams. An increase focus on youth development, the addition of recreational activities, and the implementation of a coach education program have been linked to enhanced participation rate and performance.

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)0.003
metaresearch head score (Gemma)0.003
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 score0.009
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0090.003
Scholarly communication0.0050.001
Open science0.0010.006
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.069
GPT teacher head0.350
Teacher spread0.281 · 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

Citations33
Published2017
Admission routes1
Has abstractyes

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