Mitigating Risk and Magnifying Protection: The Impacts of a Gang Intervention and Exiting Program on Criminogenic Risk Factors
Bibliographic record
Abstract
Rates of gang-related violence are high across Canada, with one quarter of homicides in 2022 connected to gangs. Individuals face numerous risk and protective factors which influence the gang disengagement process; addressing these factors is key for successful interventions. The Gang Intervention and Exiting Program (GIEP) is a holistic case management program operating in British Columbia, Canada, which targets entrenched gang members and high-risk individuals. Using a retrospective, longitudinal, single group design, the current study used generalized estimating equations to examine changes in client risk and protective factors over time. Results indicate several short-term improvements in the areas of employment, substance use behaviors, engagement with prosocial peers and family, decreased association with criminally involved family and peers, and time spent in non-gang-related activities. These findings support the use of a multipronged case management approach using a combination of civilian and law enforcement service providers to encourage gang avoidance and exit.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".