Decolonizing Community Re-entry: Effective Case Studies of Community-Led Programs and Services to Support Formerly Incarcerated Individuals in Canada
Bibliographic record
Abstract
Decolonizing re-entry programs require rethinking traditional approaches in supporting formerly incarcerated individuals and challenging colonial and deficit frameworks embedded in the criminal legal system, which often has an exclusive focus on punishment. This article names the risk factors and systemic barriers faced by equity-denied individuals during reintegration with a focus on the Canadian context. Two community-led programs in Ontario are highlighted as innovative case studies for effectively supporting reintegration of individuals exiting carceral institutions. Key characteristics of these programs which are offered by the Youth Association for Academics, Athletics, and Character Education (YAAACE) and Walls to Bridges (W2B) are outlined. Implications are discussed for enhancing effective community re-entry with a focus on amplifying the transformative impact of peer-led, trauma-informed programs that capitalize on the lived and living experiences of criminalized individuals. A series of recommendations are outlined regarding the importance of integrating Indigenous and Africentric knowledge systems and offering more programs and services rooted in trauma-informed approaches. These strategies would mitigate the unique challenges faced by Indigenous, Black, and other equity-denied identities who are disproportionately incarcerated.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.034 | 0.008 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".