Communicating the Risk of Violent and Offending Behavior: Review and Introduction to this Special Issue
Bibliographic record
Abstract
How to communicate risk of recidivism in correctional and forensic contexts has been a subject of scholarly discussion for two decades. This emerging literature, however, is sparse compared with studies on the assessment of risk for violent and offending behavior. In this special issue of Behavioral Sciences and the Law, we have gathered together empirical and review papers exemplifying promising directions and methodologies. We begin with a review of the state of the field, and lessons that can be drawn from research into medical risk assessment and risk communication, finding that many of the same principles apply to the forensic context. How risks are framed, and how numerate assessors are, affects how risk information is understood and applied. We discuss the existing research bearing on these issues, as well as the conceptual, practical, empirical, and legal implications of communicating risk using numerical or categorical risk terms. Along with the seven articles in this volume, we suggest directions for future research on measuring and communicating change, understanding and managing the statistical literacy of those who use and communicate risk assessments, and developing a theoretical framework for forensic risk communication research. We hope this volume will help integrate and invigorate research into forensic risk communication.
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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".