Assessing Dynamic Risk and Dynamic Strength Change Patterns and the Relationship to Reoffending Among Women on Community Supervision
Bibliographic record
Abstract
This study examines how dynamic risk and strength factors change over time and whether these changes are predictive of reoffending outcomes. The sample includes 2,877 Canadian women under community supervision with Service Planning Instrument reassessment data. Over a 30-month period, patterns of change in total dynamic risk and strength scores were examined. Change parameters were entered into a series of logistic regression models, linking change to three reoffending outcomes: technical violations, any new charges, and new violent charges. Overall, total dynamic risk scores decreased, and total dynamic strength scores increased over time. Change in total dynamic risk scores predicted any new charges and technical violations, whereas change in total dynamic strength scores only predicted technical violations. Findings demonstrated the utility of reassessing dynamic risk and strength scores over time and support the incorporation of strengths-based approaches with women involved in the criminal justice system.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".