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Record W2080626434 · doi:10.1080/10888691.2014.980580

Extracurricular Activity Participation and the Acquisition of Developmental Assets: Differences Between Involved and Noninvolved Canadian High School Students

2014· article· en· W2080626434 on OpenAlexaffabout
Tanya Forneris, Martin Camiré, Robert Williamson

Bibliographic record

VenueApplied Developmental Science · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicYouth Development and Social Support
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsPsychologyExtracurricular activityPositive Youth DevelopmentDevelopmental psychologyMedical educationMathematics educationPedagogyMedicine

Abstract

fetched live from OpenAlex

In order to prepare students for adulthood and responsible citizenship, most high schools offer extracurricular activities designed to facilitate the learning of a wide range of competencies. The purpose of this study was to examine how participation in a single or a combination of extracurricular school activities for high school students may impact both their developmental outcomes and their level of school engagement. Results indicated differences between youth who participated in a combination of both sport and nonsport activities as well as sport only activities compared to youth not involved in extracurricular activities on a number of developmental assets and school engagement. It is recommended that parents and adult leaders encourage and support students in their involvement in various extracurricular activities, including high school sport, in order to facilitate positive youth development.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.110
Threshold uncertainty score0.221

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0020.001
Scholarly communication0.0020.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.021
GPT teacher head0.284
Teacher spread0.263 · 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 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

Citations68
Published2014
Admission routes2
Has abstractyes

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