Young offender recidivism over a 28-month follow-up period / Bruce Cook.
Bibliographic record
Abstract
This follow-up study investigated 81 former young offenders \nof the Thunder Bay Youth Centre to determine the rate of \nrecidivism and to evaluate predictor variables. Correctional \nrecords were used and both a liberal and conservative definition \nof recidivism included in this study. Over a mean follow-up \nperiod of 28 months, there was a 58% reconviction rate under the \nCriminal Code of Canada and/or the Provincial Offenses Act. The \nrate dropped slightly to 54.3% if only Criminal Code offenses \nwere considered. Existing psychological test data and variables \ndescribed as static and dynamic predictors were investigated to \ndetermine their relationship with recidivism. Partial \ncorrelation and multiple regression techniques were used to \nreveal that supervisor ratings of the likelihood of further \ncriminal activity and aggregate sentence were statistically \nsignificant predictors of recidivism. However, these predictors \ncollectively contributed in a relatively small way to the overall \nprediction of recidivism accounting for approximately 15-16% of \nrecidivism variability. Results of this study are discussed \nalong with limitations and suggestions for future research.
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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) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.016 |
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; both teacher heads agree on what is shown here.
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".