Using Dynamic Factors to Predict Recidivism Among Women
Bibliographic record
Abstract
Using a sample of 497 Canadian women released into the community from federal prisons, this study examined the extent to which seven dynamic risk factors prospectively assessed at 6-month intervals (four waves) change over time and predict recidivism. Results obtained from a series of within-subject ANOVAs indicate that with the exception of substance abuse, all dynamic risk factors (i.e., employment, marital/family, community functioning, personal/emotional, criminal associates, and criminal attitudes) decreased among those offenders who did not recidivate. In addition, results obtained from a series of Cox regression survival analyses with time-dependent covariates also indicate that proximal assessments of dynamic risk predict recidivism more strongly than more distal assessments of dynamic risk. Employment and associates were the strongest dynamic predictors of recidivism, whereas the remaining factors were weak-to-moderate predictors of recidivism. This study lends support to the utility of repeatedly assessing dynamic risk factors among female offender populations.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".