The dynamic prediction of criminal recidivism: A three-wave prospective study.
Bibliographic record
Abstract
A three-wave, prospective panel design was used to assess the extent to which static and dynamic risk factors could predict criminal recidivism in a sample of 136 adult male offenders released from Canadian federal prisons. Static measures were assessed only once, prior to release while dynamic measures were assessed on three separate occasions: pre-release, 1 month, and 3 months post-release. Recidivism was coded during an average of 10.2-month follow-up period (SD=19.2). A series of Cox regression survival analyses with time-dependent covariates and Receiver Operator Characteristic (ROC) analyses were conducted to assess predictive validity. Although the combined static and time-dependent dynamic model (AUC=.89, CI=.81-.93) significantly (p<.01) outperformed the pure static model (AUC=.81, CI=.73-.87) the confidence intervals did overlap to some extent. Implications for dynamic risk assessment and management 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".