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Record W2160278855 · doi:10.1177/002214650604700306

Extracurricular Activities, Athletic Participation, and Adolescent Alcohol Use: Gender-Differentiated and School-Contextual Effects

2006· article· en· W2160278855 on OpenAlexaff
John P. Hoffmann

Bibliographic record

VenueJournal of Health and Social Behavior · 2006
Typearticle
Languageen
FieldSocial Sciences
TopicYouth Development and Social Support
Canadian institutionsChild, Adolescent and Family Mental Health
FundersU.S. Public Health Service
KeywordsSocioeconomic statusAssociation (psychology)PsychologyContext (archaeology)AlcoholDemographyMultilevel modelPopulationClinical psychologyDevelopmental psychologyMedicineEnvironmental health

Abstract

fetched live from OpenAlex

This research investigates the effects of extracurricular activities on alcohol use among male (n = 4,495) and female (n = 5,398) adolescents who participated in the 1990-92 National Education Longitudinal Study. Previous studies have assessed the association between extracurricular activities and alcohol use, but none have explored whether the association depends on the school context. Using a multilevel model, I examine whether school-level factors affect the relationship between involvement in athletic or nonathletic activities and changes in adolescent alcohol use from 1990 to 1992. The results indicate that the negative association between nonathletic activities and alcohol use is stronger among males in low-minority-population schools. Moreover the positive association between athletic involvement and alcohol use is stronger among females in lower-socioeconomic-status schools and males in higher-socioeconomic-status schools. I propose that these results reflect variation in high school cultures and in the resources available to schools.

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)0.003
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 score0.012
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.063
GPT teacher head0.356
Teacher spread0.292 · 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

Citations153
Published2006
Admission routes1
Has abstractyes

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