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Record W4283461657 · doi:10.1080/23750472.2022.2092538

Here today, gone tomorrow: experiences of youth who responded to an event leveraging initiative

2022· article· en· W4283461657 on OpenAlexafffund
Georgia Teare, Luke R. Potwarka, Daniel Wigfield, Chris Chard

Bibliographic record

VenueManaging Sport and Leisure · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicSport and Mega-Event Impacts
Canadian institutionsBrock UniversityUniversity of WaterlooUniversity of Ottawa
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsEvent (particle physics)Physics

Abstract

fetched live from OpenAlex

Purpose To explore experiences of youth and their parents who responded to a leveraging initiative tied to a sport event. We examined participants’ motivations, experiences engaging with the initiative, and continued involvement with a new sport.Research methods Five parent–child pairs (n = 10) who participated in the event leveraging initiative (of a total of 35) agreed to participate in semi-structured interviews about their experiences with trying a new sport opportunity.Results and Findings The initiative seemed relatively effective at encouraging youth to try a new sport but was lacking in terms of fostering continued participation in the sport. Drawing from the transtheoretical model of behavior change, our study revealed that to facilitate continued participation after initial engagement with a leveraging initiative; strategies, support, and resources should be dedicated to the program from the outset.

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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0060.003
Scholarly communication0.0030.002
Open science0.0010.004
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 head0.046
GPT teacher head0.314
Teacher spread0.268 · 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

Citations5
Published2022
Admission routes2
Has abstractyes

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