Bibliographic record
Abstract
This article discusses the implementation of the RNR principles in the Danish Prison and Probation Service. The Risk, Need, Responsibility principles were first introduced by James Bonta, a psychologist and researcher from Correctional Service Canada, at Nordisk Kriminalistmøde in Copenhagen 2010. Bonta’s research shows that using these principles in rehabilitation programs will lower the recidivism rate among offenders. With the multi-year financial agreement for the Danish Prison and Probation Service for 2013-2016, a nationwide project introducing the RNR principles was financed. The RNR project comprises two large projects in the Probation Service and in the Prisons respectively. In the RNR project in the Probation Service, 300 probation officers were trained in using the risk-and-need assessment instrument LS/RNR, and in using a newly developed model for supervision named MOSAIK. The implementation of the RNR principles in the prisons is part of a larger project improving the Intake Assessment Process in the prisons. As part of this project the prisons have established separate Intake Units, employed case managers to perform the risk-and-need assessment with new inmates (using the instrument LS/RNR), and introduced a new type of Sentence Plan. Also as part of the RNR project in the prisons a pilot on a new intervention program named MOVE is being tested in one open prison. This article mostly focuses on the project RNR in the Probation Service. It presents the evaluation design of the project as well as some of the results from the first study of the project. The study showed that even though the probation officers have been trained in using the risk-and-need assessment instrument LS/RNR not, all probation officers actually use the instrument when they supervise offenders. The article discusses some of the explanations for this.
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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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".