Assessing the Risk of Australian Indigenous Sexual Offenders Reoffending: A Review of the Research Literature and Court Decisions
Bibliographic record
Abstract
The assessment of offenders' risk of reoffending, particularly sexual reoffending, is a core activity of forensic mental health practitioners. The purpose of these assessments is to reduce the risk of harm to the public, but they are controversial and become more contentious when Australian practitioners who want to undertake such assessments in an ethically responsible way must use reliable validated instruments, disclose the limitations of their assessment methods, instruments and data to judicial decision-makers and understand how decision-makers might use their reports. The purpose of this systematic literature review was to explore the practices of Australian practitioners and courts in respect of the assessment of Australian Indigenous male sexual offenders' risk of reoffending. We could not identify an instrument that has been developed for the assessment of this population group. Australian courts differ in whether they admit and give weight to practitioners' evidence and opinions based on data obtained with non-validated instruments. We could only identify three possible predictor variables with enough quantitative support to justify including them in an instrument that could be used to assess Indigenous sexual offenders. There is a need for research regarding the validity of the instruments that practitioners use.
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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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".