Throughcare for Indigenous peoples leaving prison: Practices in two settler colonial states
Bibliographic record
Abstract
The concept of throughcare as a means to prevent recidivism continues to attract considerable attention in Australia over the last couple of years. This is particularly the case for Indigenous peoples, as the transition to life after imprisonment proves to be particularly challenging for them, resulting in high rates of recidivism and ongoing overrepresentation in Australian prisons. In this contribution, we report on research we conducted in two Australian jurisdictions. After identifying the problems in developing effective throughcare strategies for Indigenous peoples leaving prison, we turn to Canada for examples of good practice. Canada was chosen for comparison as it is also a settler colonial state, experiencing similar problems of overrepresentation of their Indigenous population in the prison. After a critical analysis of these practices, we conclude that the reasons for a problematic re-integration of Indigenous peoples are related to a tendency to impose solutions and strategies developed in the white mainstream onto Indigenous communities without acknowledging traditional cultures and structures.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.007 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".