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Record W2407496151 · doi:10.1108/jcrpp-06-2015-0018

Promises kept? A meta-analysis of gang membership prevention programs

2016· article· en· W2407496151 on OpenAlexaff
Jennifer S. Wong, Jason Gravel, Martin Bouchard, Karine Descormiers, Carlo Morselli

Bibliographic record

VenueJournal of Criminological Research Policy and Practice · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicCrime Patterns and Interventions
Canadian institutionsUniversité de MontréalSimon Fraser University
Fundersnot available
KeywordsMeta-analysisOriginalityOddsControl (management)Value (mathematics)PsychologySample (material)Computer scienceRisk analysis (engineering)MedicineSocial psychologyLogistic regressionArtificial intelligenceMachine learning

Abstract

fetched live from OpenAlex

Purpose – The purpose of this paper is to synthesize the effects of gang prevention programs on gang membership. Design/methodology/approach – The authors conducted a systematic literature review across 19 bibliographic databases and a meta-analysis of the effectiveness of these strategies. Findings – The database search resulted in 3,850 hits. Of the 162 studies that were screened in full, six involved a prevention program with outcomes commensurate for meta-analysis. Pooled log odds ratios indicate a significant, positive effect of gang prevention programs at reducing gang membership; however, sensitivity analysis demonstrates that the results are driven by the effects of a single study. Originality/value – Despite the small sample size, the current study presents the best available evidence regarding the effectiveness of gang membership prevention programs. There is a critical need in the field of gang control for rigorous evaluation of prevention strategies.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.045
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.593
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.045
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.884
GPT teacher head0.659
Teacher spread0.226 · 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 teacher head, not a consensus.

Study designTheoretical or conceptual
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

Citations17
Published2016
Admission routes1
Has abstractyes

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