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Record W2161394260 · doi:10.1037/1040-3590.15.3.413

A Multisite Comparison of Actuarial Risk Instruments for Sex Offenders.

2003· article· en· W2161394260 on OpenAlexaff
Grant T. Harris, Marnie E. Rice, Vernon L. Quinsey, Martin L. Lalumière, Douglas P. Boer, Carol Lang

Bibliographic record

VenuePsychological Assessment · 2003
Typearticle
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsQueen's UniversityCentre for Addiction and Mental HealthWaypoint Centre for Mental Health Care
Fundersnot available
KeywordsRecidivismSex offenderPsychologySex offenseRisk assessmentDeviance (statistics)PsychopathyPoison controlReceiver operating characteristicInjury preventionClinical psychologyPsychiatryDevelopmental psychologySexual abuseSocial psychologyMedical emergencyStatisticsMedicineComputer securityPersonality

Abstract

fetched live from OpenAlex

Four actuarial instruments for the prediction of violent and sexual reoffending (the Violence Risk Appraisal Guide [VRAG], Sex Offender Risk Appraisal Guide [SORAG], Rapid Risk Assessment for Sex Offender Recidivism [RRASOR] and Static-99) were evaluated in 4 samples of sex offenders (N = 396). Although all 4 instruments predicted violent (including sexual) recidivism and recidivism known to be sexually motivated, areas under the receiver operating characteristic (ROC) were consistently higher for the VRAG and the SORAG. The instruments performed better when there were fewer missing items and follow-up time was fixed, with an ROC area up to .84 for the VRAG, for example, under such favorable conditions. Predictive accuracy was higher for child molesters than for rapists, especially for the Static-99 and the RRASOR. Consistent with past research, survival analyses revealed that those offenders high in both psychopathy and sexual deviance were an especially high-risk group.

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)0.013
metaresearch head score (Gemma)0.048
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 score0.013
Threshold uncertainty score0.066

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.048
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.084
GPT teacher head0.451
Teacher spread0.368 · 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

Citations357
Published2003
Admission routes1
Has abstractyes

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