A Prospective Investigation of Factors That Predict Desistance From Recidivism for Adolescents Who Have Sexually Offended
Bibliographic record
Abstract
Current approaches to violence risk assessment are focused on the identification of factors that are predictive of future violence rather than factors that predict desistance. This is also true for the popular tools designed to predict adolescent sexual recidivism. Research on strengths-based variables with adolescents who have sexually offended that could serve a protective function is only recently underway. In the current prospective study, scores from clinician-completed assessments using the Estimate of Risk of Adolescent Sexual Offense Recidivism (ERASOR) and the parent-completed form of the Behavioral and Emotional Rating Scale (BERS-2) were evaluated in a sample of 81 adolescent males with at least one sexual offense. As expected, the ERASOR was significantly correlated with sexual recidivism over an average 3.5-year follow-up. In terms of a protective function, the Affective Strength scale of the BERS-2 was significantly negatively correlated with sexual recidivism, although it did not have incremental validity over and above the ERASOR. The BERS-2 School Functioning scale was significantly negatively correlated with nonsexual recidivism. The results are discussed in terms of previous findings and theoretical work on attachment in sexual offending behavior and implications for risk assessment practice.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".