Desistance From Sexual Offending
Bibliographic record
Abstract
For the past three decades or so, criminal justice policies have been enacted under the assumption that individuals who have been convicted of a sex offense are life course persistent sex offenders. In that context, research has been heavily focused on the assessment of risk and the prediction of sexual recidivism.Simultaneously, little to no attention has been given to the majority of individuals convicted of sex offenses who are not arrested or convicted again.Researchers have witnessed a growing gap between scientific knowledge and the sociolegal response to sexual violence and abuse. The current legal landscapecarries important social implications and significant life course impact for a growing number of individuals. More recently, theoretical and research breakthroughs in the study of desistance from crime and delinquency have been made that can help shed some light on desistance from sex offending. Desistance research, in the context of sex offending, however, represents serious theoretical, ethical, legal, and methodological challenges. To that end, this article introduces a special issue exploring current themes in desistance research by examining the life course of individuals convicted of a sexual offense while contextualizing their experiences of desistance.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".