Using Dynamic Risk to Enhance Conditional Release Decisions in Prisoners to Improve Their Outcomes
Bibliographic record
Abstract
Advances in criminal risk assessment have increased sufficiently that inclusion of valid risk measures to anchor assessments is considered a best practice in release decision-making and community supervision by many paroling authorities and probation agencies. This article highlights how decision accuracy at several key stages of the offender's release and supervision process could be further enhanced by the inclusion of dynamic factors. In cases where the timing of release is discretionary and not legislated, the utilization of a validated decision framework can improve transparency and potentially reduce decision errors. In cases where release is by statute, there is still merit in using dynamic risk assessment and case analysis to inform the assignment of release conditions, thereby attending to re-entry and public safety considerations. Finally, preliminary results from a recent study are presented to highlight the fact that community supervision outcomes may be improved by incorporating changes in dynamic risk into case planning and risk management, although this work requires replication with larger populations reflecting diverse groups of offenders. Nonetheless, these decision strategies have implications for both resource allocation and client outcomes, as outlined here. Copyright © 2016 John Wiley & Sons, Ltd.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".