Finding the Way Out: A Process Evaluation of a Gang Intervention and Exiting Program
Bibliographic record
Abstract
The Gang Intervention and Exiting Program (GIEP) was developed by law enforcement to address the rising problem of gang involvement in British Columbia, Canada. The program seeks to assist highly at-risk individuals and entrenched gang members in disengaging from gang life. The current study involved a process evaluation to assess program implementation in the context of (1) recruitment, (2) dosage, and (3) staffing. A mixed-methods approach was used, with data derived from multiple sources including 39 interviews with program staff and stakeholders, police data, and various forms of program internal records. The GIEP experienced an increase in referral frequency from a variety of sources over time, established an effective referral and outreach process, provided valued service referrals for clients (such as counseling, education, employment services, and positive mentorship), and engaged the clients’ families. Challenges regarding minimal client referrals from law enforcement, variable client-case manager contact frequency, funding restrictions on service provision, unbalanced staffing of civilian to police members, and lack of clear internal communication were also noted. Recommendations are discussed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.038 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".