Historical-Clinical-Risk Management-20 (HCR-20) Violence Risk Assessment Scheme: Rationale, Application, and Empirical Overview
Bibliographic record
Abstract
Property damage or harm to animals is not considered violence, unless carried out in a manner that is intended to cause fear of harm in others (i.e., smashing a chair, or injuring an animal,148 while stating “this is what I want to do to you!”). Acts that cause primarily psychological harm count as violence, such as stalking, unlawful connement, extortion, or kidnapping. All sexual assaults are considered violent, including those that do not cause physical harm (see Hart & Boer, Chapter 13, this volume). Including psychological harm in the denition of violence is consistent with legal principles. For instance, threats of violence, or acts that primarily cause psychological rather than physical injuries (i.e., kidnapping, extortion) can lead to legal responses such as arrest, prosecution, or involuntary civil commitment. In some countries such as Canada, “serious psychological harm” has been dened as constituting “serious bodily harm” so long as it “substantially interferes with the health or well-being of the complainant” (R. v. McCraw, 1991, p. 81). Further, including psychological harm in the denition of violence is consistent with the empirical reality that psychological damage can be as or more harmful than physical damage to a person. Acts in self-defense or the defense of others are not violence, so long as the degree of force used does not exceed that which is necessary to protect self or others. Acts that meet the denition of violence but are legally sanctioned (i.e., sports, military, law enforcement) are not considered violence unless they exceed the legal mandate that permits them.
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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 | 0.050 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.017 | 0.010 |
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".