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Record W6991125677

Examining police-involved anti-gang programs and strategies in the Lower Mainland of British Columbia

2018· article· en· W6991125677 on OpenAlexfundaboutno aff

Bibliographic record

VenueArca (British Columbia Electronic Library Network) · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicCrime Patterns and Interventions
Canadian institutionsnot available
FundersUniversity of the Fraser Valley
KeywordsNucleofectionTSG101Gestational periodHyporeflexiaProteogenomicsDemotionHemopericardiumHyperlactatemia
DOInot available

Abstract

fetched live from OpenAlex

In recent years, the Lower Mainland District (LMD) in the province of British Columbia (BC), Canada, has experienced an increase in gang violence as a result of the illicit drug trade. This major paper explores police-involved anti-gang programs and strategies for at-risk and gang-involved youth in the LMD. After reviewing the scholarly literature published in North America, this major paper examines the research on gang prevention, intervention, suppression, and comprehensive programs determined to be effective for gang-involved youth. In addition to a review of the literature, this major paper specifically examines two youth gang prevention programs, namely the Surrey Wraparound program and the Abbotsford Youth Crime Prevention Project, which are both currently funded by the National Crime Prevention Centre’s (NCPC) Youth Gang Prevention Fund (YGPF). The research findings highlight the opportunities for researchers, educators, and policy makers to implement appropriate gang prevention programs that can help reduce gang violence in the LMD.

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)0.002
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.064
Threshold uncertainty score0.463

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.006
Science and technology studies0.0080.002
Scholarly communication0.0040.001
Open science0.0020.003
Research integrity0.0010.002
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 head0.021
GPT teacher head0.255
Teacher spread0.235 · 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

Citations0
Published2018
Admission routes2
Has abstractyes

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