Tracking and managing high risk offenders: A Canadian initiative.
Bibliographic record
Abstract
The purpose of the current study was to evaluate the utility of a national initiative (the National Flagging System [NFS]) in correctly identifying high risk violent and sexual offenders and facilitating the appropriate application of preventative detention in Canada. A sample of 516 flagged offenders (FOs) was compared with 58 dangerous offenders (DOs) and 129 long-term offenders (LTOs) on demographic variables and risk assessment measures. Recidivism was also examined for a sample of FOs and LTOs. Results found many similarities among the 3 groups but FOs, on average, scored lower on structured risk assessment measures. Despite this latter finding, a significant proportion of FOs were rated as high or very high risk to reoffend according to the risk categories of the risk assessment instruments used in this study and based on percentile rankings. Violent (including sexual) reconviction rates for FOs were also significantly higher when compared to both LTOs and a sample of federal offenders. The base rate for preventative detention designations among FOs was substantially higher than the expected base rate among violent and sexual recidivists, thereby confirming the utility of the NFS. Although the NFS identifies high risk offenders, NFS coordinators would benefit from utilizing structured risk assessments when making flagging decisions.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".