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Record W2032071796 · doi:10.1002/jcop.20410

Evaluating community participation as prevention: life narratives of youth

2010· article· en· W2032071796 on OpenAlexaff
Rich Janzen, S. Mark Pancer, Geoffrey Nelson, Colleen Loomis, Julian Hasford

Bibliographic record

VenueJournal of Community Psychology · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicYouth Development and Social Support
Canadian institutionsWilfrid Laurier UniversityCentre for Community Based Research
Fundersnot available
KeywordsNarrativeFutures contractCommunity participationYouth participationPsychologyGerontologySociologyPolitical sciencePublic relationsMedicineSocioeconomicsBusiness

Abstract

fetched live from OpenAlex

Abstract Community‐based prevention programs strive to foster the composition of positive life stories, in part, by promoting active participation in community settings. This article used life narratives of youth to explore the experience of community participation and showed how such participation influenced their lives. Youth aged 18–19 years who participated in Better Beginning, Better Futures ( n =62), a community‐based prevention program, when they were aged 4–8 years, recounted stories of their lives that showed significantly higher levels of participation in community programs and greater personal impacts of that involvement compared with youth who were not involved in Better Beginnings ( n =34). Qualitative analysis of a subsample of these youth ( n =34) revealed individual and community characteristics that were instrumental in fostering positive outcomes of community participation. The findings indicated both the utility of using a narrative approach to evaluate community‐based prevention programs and the value of community participation for children and youth. © 2010 Wiley Periodicals, Inc.

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)0.007
metaresearch head score (Gemma)0.012
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 score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.003
Scholarly communication0.0040.003
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.314
GPT teacher head0.546
Teacher spread0.232 · 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

Citations13
Published2010
Admission routes1
Has abstractyes

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