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Youth Gang Exit

2016· book-chapter· en· W2501231839 on OpenAlexaffabout
Laura Dunbar

Bibliographic record

VenueAdvances in psychology, mental health, and behavioral studies (APMHBS) book series · 2016
Typebook-chapter
Languageen
FieldSocial Sciences
TopicCrime Patterns and Interventions
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsVariety (cybernetics)CriminologyArgument (complex analysis)Process (computing)Order (exchange)Psychological interventionSociologyPsychologyComputer scienceMedicineArtificial intelligenceBusiness

Abstract

fetched live from OpenAlex

Youth gangs and their members have been studied in a variety of contexts; however the issue of desistance has received less attention. This chapter seeks to address this gap. In order to situate the material to be covered, the chapter begins with an introduction to the topic of youth gangs. Next, an overview of the concept of desistance and how it is measured is provided. Following that is a review of some prominent criminological perspectives demonstrating that leaving the gang is a complex process involving the interaction of a combination of factors. The process of desistance and methods for leaving the gang are also briefly discussed. Several approaches have been developed to address youth gangs and their members. These approaches are reviewed and different interventions under these headings are discussed with Canadian examples provided. Finally, an argument for the development of a comprehensive strategy for youth gang exit is presented.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.019
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0190.003

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 head0.147
GPT teacher head0.500
Teacher spread0.353 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2016
Admission routes2
Has abstractyes

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