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Record W2105732081 · doi:10.1177/0093854814523003

Prediction of General and Violent Recidivism Among Mentally Disordered Adult Offenders

2014· article· en· W2105732081 on OpenAlexaff
Donaldo D. Canales, Mary Ann Campbell, Ran Wei, Angela E. Totten

Bibliographic record

VenueCriminal Justice and Behavior · 2014
Typearticle
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsRecidivismPsychologyPoison controlHuman factors and ergonomicsInjury preventionPredictive validityRisk assessmentSuicide preventionPsychiatryClinical psychologyOccupational safety and healthMedicineComputer securityEnvironmental healthComputer science

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.577
Threshold uncertainty score0.904

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.038
GPT teacher head0.301
Teacher spread0.263 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations68
Published2014
Admission routes1
Has abstractyes

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