CRIME PREVENTION IN ABORIGINAL COMMUNITIES
Bibliographic record
Abstract
The purpose of this report is to provide information to help the Aboriginal Justice Inquiry Implementation Commission make recommendations concerning ways of reducing crime rates in Aboriginal communities in Manitoba. Both the Aboriginal Justice Inquiry and the Implementation Commission have received ample evidence that crime rates are very high in many Aboriginal communities and among Aboriginal people living outside of these communities. Victimization among Aboriginal people is also disproportionately high. While there is evidence that this over-representation is partly due to systemic discrimination by the justice system, it is also clear that some Aboriginal communities do have very high crime rates. The cost of this crime to communities, victims, and offenders is so high that there must be more emphasis on prevention. The first section of the report will provide a definition of crime prevention and briefly discuss how programs should be planned and implemented. The second section will provide an analysis of what works in crime prevention. Very little evaluation has been done concerning crime prevention in an Aboriginal context, so I will review current knowledge of what works in the broader society as many of the lessons learned will also apply to Aboriginal communities. The third section will describe some of the more promising crime prevention programs that are being used within Aboriginal communities. It will also look at some of the issues involved in developing effective programs in Manitoba and make some recommendations for the Commission.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".