Tracking High-Risk, Violent Offenders: An Examination of the National Flagging System
Bibliographic record
Abstract
The present study investigated the effectiveness of the Canadian National Flagging System (NFS), a policy initiative intended to identify offenders who are judged to be suitable candidates for a Dangerous Offender (DO) or a Long-Term Offender (LTO) application. Analyses comparing the profiles of 256 flagged offenders and 97 known high-risk, violent offenders indicated that the flagged offenders generally showed less serious and persistent criminality characteristics than the known high-risk, violent offenders. However, scores on actuarial measures of risk demonstrated that both groups comprised especially high-risk offenders. Furthermore, the violent and/or sexual reconviction rates of the flagged offenders were significantly higher than those reported among the typical Canadian male federal offender population. Judged against our expectations, the base rate of DO/LTO designations among the violent/sexual recidivist flagged offenders was also much higher than the one estimated among the general high-risk, violent offender population in Canada. As a whole, the findings suggested that the NFS was successful in appropriately identifying offenders who pose a risk to the community as well as in subsequently responding to this threat by facilitating the use of the DO/LTO provisions. Recommendations for the development of guidelines to assist criminal justice professionals in screening, monitoring, and processing high-risk, persistent offenders are made.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".