Applying Crime Prevention and Health Promotion Frameworks to the Problem of High Incarceration Rates for Aboriginal and Torres Strait Islander Populations: Lessons from a Case Study from Victoria
Bibliographic record
Abstract
This article examines what kinds of policy reforms are required to reduce incarceration rates of Aboriginal and Torres Strait Islander people through a case study of policy in the Australian state of Victoria. This state provides a good example of a jurisdiction with policies focused upon, and developed in partnership with, Aboriginal communities in Victoria, but which despite this has steadily increasing incarceration rates of Indigenous people. The case study consisted of a qualitative analysis of two key justice sector policies focused upon the Indigenous community in Victoria and interviews with key justice sector staff. Case study results are analysed in terms of primary, secondary, and tertiary crime prevention; the social determinants of Indigenous health; and recommended actions from the Ottawa Charter for Health Promotion. Finally, recommendations are made for future justice sector policies and approaches that may help to reduce the high levels of incarceration of Aboriginal and Torres Strait Islander people.
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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 | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.019 | 0.010 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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".