What Works (or Does Not) in Community Risk Management for Persons Convicted of Sexual Offenses? A Contemporary Perspective
Bibliographic record
Abstract
Contemporary data from the United States show that rates of sexual offending and reoffending have been in steady decline for decades. Nonetheless, nonprofessionals continue to view sexual violence as a community safety issue fraught with risk and uncertainty. The past 30 years have been witness to considerable research and practice in the assessment, treatment, and risk management of persons who have sexually offended. Gains have also been made in regard to prevention and citizen education. Modern day technologies include actuarial risk assessment instruments, measures of criminogenic need and treatment progress, refinements to treatment processes, and the establishment of evidence-based models. Legislative authorities in the United States and elsewhere have also attempted to affect risk in the community with, perhaps, lesser degrees of success. This article reviews current policies and practices, with a specific focus on what happens when offenders are released to the community (e.g., how public policies intended to track offenders and/or restrict their movements can negatively affect community reintegration). Comprehensive approaches to community sexual offender management are examined in addition to suggestions of unique approaches intended to ensure citizen buy-in and engagement.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".