Combining Static and Dynamic Recidivism Risk Information Into the Five-Level Risk and Needs System: A New Zealand Example
Bibliographic record
Abstract
Communicating recidivism risk is individualized to each assessment. Labels (e.g., high, low) have no standardized meaning. In 2017, the Council of State Governments Justice Center (CSGJC) proposed a framework for standardized communication, but balancing the framework’s underlying principles of effective risk communication (and merging static and dynamic information) adds complexity. In this study, we incorporated dynamic risk scores that case managers rated among a routine sample of adults on parole in New Zealand ( N = 440) with static risk scores into the Five-Level Risk and Needs System. Compared with static risk only, merging tools (a) enhanced concordance with the recidivism rates proposed by CSGJC for average and lower-risk individuals, (b) diminished concordance for higher-risk individuals, yet (c) improved conceptual alignment with the criminogenic needs domain of the system. This example highlights the importance of attending to the underlying principles of effective risk communication that motivated the development of the system.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".