Assessing the Risk and Needs of Supervised Sexual Offenders
Bibliographic record
Abstract
Effective intervention with offenders requires accurate identification of their risk-relevant propensities. In this prospective study, 139 Canadian community supervision officers were trained to assess the risk factors and criminogenic needs of adult male sexual offenders using structured risk tools. Recidivism outcomes were recorded for 768 offenders (average age of 41 years, approximately half had child victims, 14% Aboriginal) during an average 7-year follow-up period. All forms of recidivism (sexual, violent, any) were predicted by sex crime specific risk tools based on static, historical factors (Static-99R; Static-2002R) and by tools designed to assess psychologically meaningful risk factors of sexual offenders (STABLE-2000; STABLE-2007). Professional overrides of the Static-99 scores did not improve predictive accuracy. STABLE-2007 scores added incrementally over STATIC scores for all recidivism outcomes, but only for complete cases, suggesting meaningful variation in the extent to which community supervision officers can assess psychologically meaningful risk factors for sexual offenders.
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 | 0.001 | 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 | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".