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Record W2955989847 · doi:10.60770/85c5-9r25

Adolescents in sports: the all-delinquent team

2019· dissertation· en· W2955989847 on OpenAlexaff
Colm McCabe

Bibliographic record

VenueMount Royal University Institutional Repository (Mount Royal University) · 2019
Typedissertation
Languageen
FieldMedicine
TopicSports injuries and prevention
Canadian institutionsMount Royal University
Fundersnot available
KeywordsPsychologyApplied psychologyCriminology

Abstract

fetched live from OpenAlex

Analyzed through the lens of social-bond theory, this thesis examines the relationship between sports participation and delinquency among adolescents. The purpose of this thesis is to better understand whether sports can serve as an effective intervention strategy for policy makers, government agencies and criminal justice branches that deal directly with at risk-youth or offenders who can benefit from sports-related programs. Through the use of a meta-analysis methodological design, the findings uncovered through common literature will reflect the extent to which social-bond theory can sufficiently explain delinquency among athletes. Traditionally, sports-participation and physical activity have been connected to prosocial stereotypes and the belief that adolescents will develop character-building morals. Although many situations including sports-participation are mainly positive across most facets, there is further evidence to suggest that unintended, antisocial-developing consequences can arise from participation in sports-related activities. Jock identity and unstructured socializing were highlighted as major factors for delinquency among athletes, whereas the pedagogical sports-environment serves as a possible deterrence model for delinquency. With further extensive research into this topic, the development of a pedagogical sports model can provide more athletes with an exceptional prosocial experience. Similarly, sports participation and sports-related intervention strategies can be utilized to address and combat youth-crime.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.685
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.007
GPT teacher head0.212
Teacher spread0.205 · 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 teacher head, not a consensus.

Study designObservational
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

Citations0
Published2019
Admission routes1
Has abstractyes

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