Criminogenic and Non-Criminogenic Factors and Their Association With Reintegration Success for Individuals Under Judicial Orders in Canada
Bibliographic record
Abstract
Justice-involved individuals who reach the end of their full prison sentence no longer benefit from the supervision and rehabilitation services offered by probation or parole. Some of these individuals, who have been assessed to be a high risk for sexual and violent reoffending and deemed to pose a significant violence risk in the community if released, are placed on a judicial order in Canada, and police are asked to supervise and manage the risk of these individuals. In the current study, the files of 45 high-risk, justice-involved individuals, who completed their sentences, were released from a Canadian prison into the province of Alberta, and supervised by police under a judicial order, were reviewed for the presence of criminogenic and non-criminogenic needs over the first year of release. The associations between these needs and proximal reintegration outcomes were examined. Our findings revealed that basic needs and responsivity issues were prevalent in the early part of supervision; however, these factors were unrelated to proximal reintegration success. In contrast, criminogenic needs were prevalent and associated with poorer reintegration. This study reinforces the role that police can play in monitoring and addressing criminogenic needs with the goal of reducing recidivism and employing the help of non-police supports to address non-criminogenic needs.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".