Onashowewin and the Promise of Aboriginal Diversionary Programs
Bibliographic record
Abstract
This article focuses on the use of Indigenous diversionary programming by Onashowewin, an Indigenous non-profit organization in Winnipeg.An analysis of 100 case files finds a recidivism rate of 30%.That is a very positive outcome, especially when compared to numerous studies that have found high recidivism rates for Indigenous offenders.What is particularly encouraging is the possibility that programs like Onashowewin can lead Indigenous persons to more positive lifestyles after their earliest contacts with the justice system, and thereby avert patterns of reoffending that frequently lead to incarceration in federal penitentiaries.Onashowewin also incorporates Indigenous cultures and spirituality into its programming.Part of Onashowewin's promise is the ability to contribute to cultural revitalization, even if limited in scale.Onashowewin, and other programs like it, can also provide a foundation upon which Indigenous selfdetermination can eventually be built.
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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 | 0.011 | 0.002 |
| 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".