Prediction of General and Violent Recidivism Among Mentally Disordered Adult Offenders
Bibliographic record
Abstract
The present investigation examined the predictive validity of the Level of Service/Risk–Need–Responsivity (LS/RNR) instrument for general and violent recidivism in a sample of 138 community-supervised adult mentally disordered offenders. The General Risk/Need section was strongly predictive of general recidivism, whereas the Specific Risk/Need section most strongly predicted violent recidivism. Among males, the General Risk/Need section produced a large effect size for general recidivism, whereas general and violent outcomes for females were best predicted by the Specific Risk/Need section. Across diagnostic subgroups, the General and Specific Risk/Need sections predicted general but not violent recidivism; however, many subgroups were small, highlighting a need for replication research with larger samples. The Other Client Issues and Special Responsivity Considerations sections did not significantly inform recidivism prediction. Broadly interpreted, the overall pattern supports the LS/RNR instrument as valid for use with mentally disordered 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".