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Record W2949685955 · doi:10.22329/csw.v9i1.5763

Do Community Arts Programs Promote Positive Youth Development?

2019· article· en· W2949685955 on OpenAlexaffvenue
Robin Wright, Lindsay John, Ramona Alaggia, Eric Duku, Tanya Morton

Bibliographic record

VenueCritical Social Work · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicYouth Development and Social Support
Canadian institutionsMcMaster UniversityUniversity of TorontoUniversity of Windsor
Fundersnot available
KeywordsThe artsAttendancePsychosocialPositive Youth DevelopmentPsychologyCommunity developmentObservational studyDevelopmental psychologyMedical educationMedicinePolitical sciencePsychiatry

Abstract

fetched live from OpenAlex

This study reports on the multi-method longitudinal examination of a structured arts program (combination of theatre, visual and media arts) for youth, aged 9 to 15 years, from a low-income community in Hillsborough County in Tampa, Florida. Evaluated were the extent to which the community-based organization could recruit and retain youth in the program and whether they demonstrated improvement with respect to artistic ability and psychosocial indicators. The results suggest successful recruitment and sustained attendance rates. The study employed a multilevel growth curve analyses of observational and behavioral outcomes which showed significant gains in artistic and social skills, and a significant reduction in emotional problems. The contention that community arts programs promote positive youth development is supported by this study.

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)0.006
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: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.000
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 head0.072
GPT teacher head0.346
Teacher spread0.275 · 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

Citations7
Published2019
Admission routes2
Has abstractyes

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