Violence Risk Assessment Tools: Overview and Critical Analysis
Bibliographic record
Abstract
One of the important inuences on contemporary conceptions of risk assessment is the risk/ needs/responsivity (RNR) model described by Canadian researchers (Andrews & Bonta, 2006; Andrews, Bonta, & Hoge, 1990; Andrews, Bonta, & Wormith, 2006). is involves the appraisal of three related domains. Risk refers to the probability that the examinee will engage in a certain kind of behavior in the future, typically either violence/violent oending, or criminal oending of any kind, with higher-risk individuals receiving more intensive intervention and management services. is kind of risk classication has typically employed static risk factors, which do not change through planned intervention, although some tools (for example, the Level of Service Inventory [LSI] measures) (see Andrews & Bonta, 2001; Andrews, Bonta & Wormith, 2004) use both static risk factors and risk-relevant needs. Needs are variables describing decits which are related to the probability of such targeted outcomes; they are composed of dynamic risk factors (called criminogenic needs in the RNR model) or protective factors that have the potential to change through such planned intervention. Responsivity refers to the extent to which an individual is likely to respond to intervention(s) designed to reduce the probability of the targeted outcome behavior.
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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 | 0.114 | 0.194 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.037 | 0.015 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".