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Record W4416675821 · doi:10.1080/15614263.2025.2594776

Joining forces: operational strengths and challenges of a joint police-civilian gang intervention and exiting program

2025· article· en· W4416675821 on OpenAlex
Chelsey Lee, Jennifer S. Wong

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenuePolice Practice and Research · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicPolicing Practices and Perceptions
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsIntervention (counseling)Joint (building)

Abstract

fetched live from OpenAlex

Gang interventions often rely on police for intelligence but seldom incorporate them into treatment delivery despite the risks that gang members might pose to civilian case workers. The Gang Intervention and Exiting Program (GIEP), implemented in British Columbia, Canada, uses an individualized case management approach and pairs civilian case managers with police officers to provide client services and supports to encourage gang exiting and prosocial behaviors. The current study examined the operational experiences of program administrators and stakeholders involved with the GIEP via 38 semi-structured qualitative interviews. The thematic analysis yielded a series of strengths and challenges related to (a) program delivery and supports, (b) resources, and (c) administration and structure. The findings suggest that the civilian-police pairing has multiple benefits, but the direct role of police also brings challenges that may require additional consideration for program success.

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.

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)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.509
Threshold uncertainty score0.939

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.196
GPT teacher head0.532
Teacher spread0.336 · 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