A Survey of Community Notifications of Justice-Involved Persons Being Released from Incarceration in Canada
Bibliographic record
Abstract
The practice of notifying the public about the release of a justice-involved individual into a community has become fairly common in Canada and other countries. It is assumed that these notifications help the public and increase public safety. Despite this being seen as a common service to the public, what information should be included in a notification is less understood. The present study surveys 177 publicly accessible community notifications across Canada regarding the release of justice-involved individuals from correctional facilities into the community. Notifications were reviewed and coded for the presence of demographic information, risk descriptors, offence and incarceration histories, and treatment and supervision information. The most commonly reported information included some physical descriptors and photos of the released individual, as well as criminal history and risk level. However, treatment history and details about upcoming supervision were less commonly reported. What was reported differed by region (western vs. eastern part of Canada), where the notification was issued, and the source (government and police vs. news media) that issued it. The lack of consistency in these notifications has implications for fairness, reintegration, public confidence in the justice system, and cross-provincial risk management practices.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".