Handbook of Violence Risk Assessment
Bibliographic record
Abstract
Heilbrun, Yasuhara, Shah, Violence Risk Assessment Tools: Overview and Critical Analysis. DeMatteo, Edens, Hart, The Use of Measures of Psychopathy in Violence Risk Assessment. Part I: Juvenile Risk. Augimeri, Enebrink, Walsh, Jiang, Gender-specific Childhood Risk Assessment Tools: Early Assessment Risk Lists for Boys (EARL-20B) and Girls (EARL-21G). Borum, Lodewijks, Bartel, Forth, Structured Assessment of Violence Risk in Youth (SAVRY). Hoge, Youth Level of Service/Case Management Inventory. Part II: Adult Risk. Rice, Harris, Hilton, The Violence Risk Appraisal Guide and Sex Offender Risk Appraisal Guide for Violence Risk Assessment and the Ontario Domestic Assault Risk Assessment and Domestic Violence Risk Appraisal Guide for Wife Assault Risk Assessment. Wong, Olver, Two Treatment- and Change-oriented Risk Assessment Tools: The Violence Risk Scale and Violence Risk Scale - Sexual Offender Version. Douglas, Reeves, Historical-clinical-risk Management-20 (HCR-20) Violence Risk Assessment Scheme: Rationale, Application, and Empirical Overview. Monahan, The Classification of Violence Risk. Andrews, Bonta, Wormith, The Level of Service (LS) Assessment of Adults and Older Adolescents. Kropp, Gibas, The Spousal Assault Risk Assessment Guide (SARA). Anderson, Hanson, Static-99: An Actuarial Tool to Assess Risk of Sexual and Violent Recidivism Among Sexual Offenders. Hart, Boer, Structured Professional Judgment Guidelines for Sexual Violence Risk Assessment: The Sexual Violence Risk-20 (SVR-20) and Risk for Sexual Violence Protocol (RSVP).
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.
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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.087 | 0.053 |
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".