Not in My Backyard: Public Sex Offender Registries and Public Notification Laws
Bibliographic record
Abstract
In Canada, the community risk management strategy utilized does not include a publicly available sex offender registry. While there is a non-public national sex offender registry for police investigation purposes, in recent years, there has been ongoing pressure to import American sex offender registry and notification (SORN) laws in Canada. Such pressure has been supplemented by the emergence of an increasing number of individual initiatives mimicking these policies by providing personal information through various means about individuals convicted of sex crimes. Since their inception, the American SORN laws have been the subject of much debate among scholars, policy makers, and the Victims’ Rights Movement. Despite the popularity of American-style SORN laws among certain circles, policy evaluation research has not presented convincing evidence that such measures carry a crime prevention impact. In fact, American scholars have highlighted several issues, problems, and challenges that are overlooked by promoters of SORN policies in Canada. The mere presence of such policies in the U.S. should not be interpreted as an indication of good policing of sexual violence and abuse. A concerted scientific approach rather than punitive populism is much needed to tackle the issue of sexual violence and abuse in Canada.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.031 | 0.105 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.008 | 0.015 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".