The Risk-Need-Responsivity (RNR) Model
Bibliographic record
Abstract
The risk-need-responsivity (RNR) model has been widely regarded as the premier model for guiding offender assessment and treatment. The RNR model underlies some of the most widely used risk-needs offender assessment instruments, and it is the only theoretical model that has been used to interpret the offender treatment literature. Recently, the good lives model (GLM) has been promoted as an alternative and enhancement to RNR. GLM sets itself apart from RNR by its positive, strengths-based, and restorative model of rehabilitation. In addition, GLM hypothesizes that enhancing personal fulfillment will lead naturally to reductions in criminogenic needs, whereas RNR posits the reverse direction. In this article the authors respond to GLM’s criticisms of RNR and conclude that little substance is added by GLM that is not already included in RNR, although proponents of RNR may learn from the popular appeal that GLM, with its positive, strength-based focus, has garnered from clinicians over the past decade.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".