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Record W2057791573 · doi:10.1177/0093854806291157

Actuarial Assessment of Risk for Reoffense Among Adult Sex Offenders

2007· article· en· W2057791573 on OpenAlexaff
Calvin M. Langton, Howard E. Barbaree, Michael C. Seto, Edward J. Peacock, Leigh Harkins, Kevin T. Hansen

Bibliographic record

VenueCriminal Justice and Behavior · 2007
Typearticle
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsMinistry of Community Safety and Correctional ServicesUniversity of TorontoCentre for Addiction and Mental Health
Fundersnot available
KeywordsRecidivismRisk assessmentPsychologySex offenderSex offensePoison controlHuman factors and ergonomicsSample (material)Injury preventionDemographyActuarial scienceForensic engineeringRisk analysis (engineering)EngineeringClinical psychologyMedicineSexual abuseMedical emergencyComputer securityComputer scienceSociology

Abstract

fetched live from OpenAlex

This study extended previous research comparing a set of widely employed actuarial risk assessment schemes as well as a new instrument, the Static-2002, in a sample of 468 sex offenders followed for an average of 5.9 years. All of the risk assessment instruments (Violence Risk Appraisal Guide [VRAG], Sex Offender Risk Appraisal Guide [SORAG], Rapid Risk Assessment for Sex Offense Recidivism [RRASOR], Static-99, Static-2002, and Minnesota Sex Offender Screening Tool-Revised [MnSOST-R]) were found to predict the recidivism outcomes for which they were designed. Although significant, indices of accuracy were generally lower than those reported by the developers of these instruments, even under conditions that have been shown to optimize predictive performance. For serious recidivism, the predictive accuracy of the Static-2002 and SORAG was significantly superior to that of the RRASOR, and the SORAG was significantly superior to the MnSOST-R as well. There were no significant differences among instruments in accuracy of predicting sexual recidivism.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.046
GPT teacher head0.381
Teacher spread0.334 · 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 source (direct Gemma or distilled Codex), 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

Citations129
Published2007
Admission routes1
Has abstractyes

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