Risky Business: Predicting Recidivism
Bibliographic record
Abstract
Society has become more and more preoccupied with both the ascertainment and avoidance of risk. This preoccupation has permeated the criminal justice system and courts are increasingly being required to evaluate the risk of reoffending, when considering the imposition of sentences and other control measures, particularly in regard to crimes of violence and sexual offending. This has resulted in the need for reliable risk assessment tools and expert evidence to assist judges in their task. While health professionals have willingly provided such assistance, it is apparent that even the current generation of risk assessment tools are not without their limitations. This has led to some commentators suggesting that such tools merely provide a veil of science over what really are moral and ethical questions as to which offenders pose an unacceptable danger to society. While not subscribing to that view, this article emphasises the need for experts to convey the limitations of such instruments clearly to the courts. It also suggests that any tools used must be aligned with the statutory criteria and that such tools must be used in combination with an individualised assessment of risk for each offender. The reasoning process must be transparent and set out clearly for the court. As sentences based on risk have the potential to place major restrictions on the rights of offenders, courts must have as much assistance as possible in the task of balancing the human rights of offenders with the risk to public safety posed by such offenders. R v Peta [2007] 2 NZLR 627 (CA) is used as a case study to illustrate both what can go wrong, as well as an example of best practice in this often precarious balancing exercise.
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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 | 0.001 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".