EXITING GANGS: EXAMINING PROCESSES AND BEST PRACTICE WITHIN AN ALBERTA CONTEXT
Bibliographic record
Abstract
Gangs and gang-related crime have been an increasing concern in Alberta in recent decades. Gang exit strategies have been identified in the Alberta Gang Reduction Strategy (Government of Alberta, 2010) as a key activity in reducing gang-related violent crime and violence in the province. The purpose of this article is to explore available academic and gray literature on gang exit to support the development of gang exit interventions in the province. Findings from the review point to the diversity and complexity of gang involvement and membership, and the consequent need for multi-dimensional approaches to gang exit. Exit programs must address the root causes of membership, and identify and address barriers to pro-social activities. The complexity of gang membership also requires a strategic approach to programming that includes single case management, intensive training, and targeted outreach, as well as multiple systems involvement. Importantly, as Alberta moves forward with its Gang Reduction Strategy, systematic, comprehensive research studies on the “gang problem” in Alberta and its associated impact on the community are vital for the development of effective intervention strategies.
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 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 | 0.018 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.018 | 0.010 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| 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".