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Record W4390728218 · doi:10.1123/kr.2023-0024

The Dropout From Youth Sport Crisis: Not as Simple as It Appears

2024· article· en· W4390728218 on OpenAlexaff
Anthony Battaglia, Gretchen Kerr, Katherine A. Tamminen

Bibliographic record

VenueKinesiology Review · 2024
Typearticle
Languageen
FieldPsychology
TopicMotivation and Self-Concept in Sports
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsDisengagement theoryDropout (neural networks)CLARITYPsychologyPositive Youth DevelopmentNarrativeRelevance (law)Social psychologyDevelopmental psychologyApplied psychologyPolitical scienceGerontologyMedicine

Abstract

fetched live from OpenAlex

Given the documented benefits associated with organized sport and thus the assumption that youth who leave sport are losing out on developmental benefits, dropout has been predominantly framed as a crisis to be solved. Throughout this paper we aimed to challenge the overarching narrative of youth dropout from organized sport as a negative outcome only by highlighting the complexity of youth sport experiences and participation patterns. First, we highlight the lack of conceptual clarity regarding the term “dropout” and question its relevance for describing youth’s sport experiences. Next, we discuss how declines in organized sport participation may reflect developmentally appropriate transitions in sport and broader physical activity for youth and across the life span. Finally, we suggest that, at times, disengagement may be a positive and protective outcome for youth when the sport environment is harmful. Recommendations for future research and practice are provided to advance the understanding of youth sport experiences and participation patterns.

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)0.005
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0030.003
Open science0.0010.003
Research integrity0.0010.003
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 head0.042
GPT teacher head0.357
Teacher spread0.315 · 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

Citations22
Published2024
Admission routes1
Has abstractyes

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