Violence Risk Assessment Tools: Overview and Critical Analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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) | 0.021 | 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 it