Adjusting Actuarial Violence Risk Assessments Based on Aging or the Passage of Time
Bibliographic record
Abstract
Two studies herein address age, the passage of time since the first offense, time spent incarcerated, or time spent offense free in the community as empirically justified postevaluation adjustments in forensic violence risk assessment. Using three non-overlapping samples of violent offenders, the first study examined whether any of three variables (time elapsed since the first offense, time spent incarcerated, and age at release) were related to violent recidivism or made an incremental contribution to the prediction of violent recidivism after age at first offense was considered. Time since first offense and time spent incarcerated were uninformative. Age at release predicted violent recidivism but not as well as age at first offense, and it afforded no independent incremental validity. For sex offenders, age at first offense improved the prediction of violent and sexual recidivism. In the second study, time spent offense-free while at risk was related to violent recidivism such that an actuarial adjustment for the Violence Risk Appraisal Guide could be derived. The results support the use of adjustments (based on the passage of time) to actuarial scores, but only adjustments that are themselves actuarial.
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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 | 0.007 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".