An appraisal of the risk–need–responsivity (RNR) model of offender rehabilitation and its application in correctional treatment
Bibliographic record
Abstract
The science of effective offender rehabilitation remains a very young field: dominated theoretically and empirically by the work of a small group of Canadian psychologists. Their achievements include the ‘what works’ research literature, and the RNR model of offender rehabilitation. First disseminated in 1990, over the following 20 years, the Risk, Need and Responsivity Principles became the core of the theoretical framework used in those correctional systems around the world that use science as a basis for offender rehabilitation. This paper evaluates the strengths and weaknesses of the RNR model as a Level I rehabilitation framework. It proposes that unrealistic expectations and mistranslations of the model into practice are contributing to concerns about its validity and utility, and stifling needed innovation in the development both of mid‐level treatment resources, and of RNR‐adherent interventions. It concludes that although the RNR model's empirical validity and practical utility justify its place as the dominant model, it is not the ‘last word’ on offender rehabilitation; there is much work still to be done.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.020 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.018 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".