The War on Sex Offenders: Community Notification in Perspective
Bibliographic record
Abstract
This article explores the contemporary phenomenon of “naming and shaming” sex offenders. Community notification laws, popularly known as Megan's Law, which authorise the public disclosure of the identity of convicted sex offenders to the community in which they live, were enacted throughout the United States in the 1990s. A public campaign to introduce “Sarah's Law” has recently been launched in Britain, following the death of eight-year old Sarah Payne. Why are sex offenders, and certain categories of sex offenders, singled out as targets of community notification laws? What explains historical variability in the form that sex offender laws take? We address these questions by reviewing the sexual psychopath laws enacted in the United States in the 1930s and 40s and the sexual predator and community notification laws of the 1990s, comparing recent developments in the United States with those in Britain, Canada, and Australia. We consider arguments by Garland, O'Malley, Pratt, and others on how community notification, and the control of sex offenders more generally, can be explained; and we speculate on the likelihood that Australia will adopt community notification laws.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.021 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".