The Risk–Need–Responsivity Model of Assessment and Human Service in Prevention and Corrections: Crime-Prevention Jurisprudence
Bibliographic record
Abstract
The general personality and social psychology underlying the Risk–Need–Responsivity (RNR) model of rehabilitation recognizes the importance of the personal, interpersonal, and relatively automatic sources of control over human behaviour as well as the power of cognitive-social-learning approaches to interpersonal influence in many social settings. In terms of both prediction and intervention, the RNR model has impressive but limited research support and is widely implemented, albeit with mixed support in routine correctional practice. This article suggests that RNR and the psychology that underlies it may also assist justice agencies and the courts through crime-prevention jurisprudence (CPJ). Always in the context of ethical, legal, just, and otherwise normative interventions, the first task is to help keep low-risk cases low risk and not interfere with existing strengths. The second task is to identify moderate and higher-risk cases and arrange crime-prevention activities consistent with ethical, legal, and just applications of the principles of RNR. Not the least of the benefits is the provision of an evidence-based set of crime-prevention practices as well as a language system that will facilitate inter-agency and intra-agency communication both within and outside of the justice, court, and correctional systems.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.002 | 0.020 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".