Sex Offender Registries and Public Notification in the United States and Canada
Bibliographic record
Abstract
This chapter explores sex offender registries (SORs) and public notification (PN) in the United States and Canada. The process of maintaining a balance between protection of the public and an individual’s right to privacy becomes increasingly difficult when the offender is considered to be dangerous or “morally tainted.” Mechanisms, such as SORs and PN, have been implemented as a political response to highly publicized cases of sexual victimization. Supporters of these interventions claim that such supervisory methods will deter sexual offenders from reoffending while providing information on which the public may act to protect its children and others who may be vulnerable and at the same time giving an investigative tool with which police can more efficiently “solve” sexual offenses. In contrast, opponents of SORs and community notification stress concerns about a discriminatory aspect to the regulations that specifically targets sex offenders when there is an absence of evidence that sex offenders, as a general category, are at a greater risk to reoffend than other categories of offenders. Additionally, critics have raised concerns about the ethics and legality of infringing on the rights of a segment of the population who have already served their sentence and paid the penalty for their misconduct.
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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 | 0.003 | 0.006 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 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".