Current issues in the assessment and treatment of sexual offenders
Bibliographic record
Abstract
The present paper reviews what is currently known about sexual abuse and the treatment of the offenders. Sexual offending is primarily committed by males and as many as 50% of females and 33% of males are sexually victimized in some way during their life. While some of these acts involve minimally intrusive behaviours (e.g. exhibitionists), many involve forced vaginal or anal intercourse and varying degrees of associated physical violence and deliberate humiliation of the victim. The effects on the victim can be severe and long-lasting. Current theories of the aetiology of these behaviours incorporate biological and developmental factors occurring within a socio-cultural context that fails to discourage sexual abuse. Experience is said to create a vulnerability that in some men finds expression in sexually abusive behaviour. Appropriate assessment targets the issues that are addressed in treatment, including: cognitive distortions, sexual behaviours and attitudes, social functioning and substance abuse. In addition, treatment also attempts to generate a set of relapse prevention plans for each offender. While some reports have not found benefits for treatment, the majority of acceptable studies have demonstrated clear reductions in re-offending among treated sexual offenders. Copyright © 2000 John Wiley & Sons, Ltd.
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 | 0.072 | 0.121 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.006 | 0.016 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.009 | 0.007 |
| Research integrity | 0.016 | 0.013 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".