How can forensic systems improve justice for victims of offenders found not criminally responsible?
Bibliographic record
Abstract
Controversy has arisen surrounding findings of not criminally responsible (NCR) or not guilty by reason of insanity (NGRI) in recent years. In some countries, the debate has been driven by the concerns of victims, who are seeking greater information on discharge, accountability on the part of the offender, and involvement in the disposition of NCR or NGRI perpetrators. Their demands raise questions about proportionality between the seriousness of the index offense and the disposition imposed, the place of retribution in the NCR regimen, and the ethics-related challenges that emerge from this tension. We conducted a literature review focused on the relationship of victims to NCR and NGRI processes. The literature is limited. However, theoretical reasoning suggests that interventions based on restorative justice principles reduce persistently negative feelings and increase a sense of justice for victims of criminally responsible defendants. Opportunities and problems with extending such processes into the area of mentally abnormal offenders are discussed.
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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 | 0.013 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 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".