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Record W1938257655 · doi:10.1177/0093854815602094

Assessing the Risk and Needs of Supervised Sexual Offenders

2015· article· en· W1938257655 on OpenAlexaffabout
R. Karl Hanson, L. Maaike Helmus, Andrew Harris

Bibliographic record

VenueCriminal Justice and Behavior · 2015
Typearticle
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsPublic Safety Canada
Fundersnot available
KeywordsRecidivismRisk assessmentPsychologySex offenseHuman factors and ergonomicsPoison controlSuicide preventionInjury preventionClinical psychologyIntervention (counseling)Occupational safety and healthPsychiatrySexual abuseMedicineComputer securityMedical emergencyComputer science

Abstract

fetched live from OpenAlex

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 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)0.001
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.493
Threshold uncertainty score0.665

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.128
GPT teacher head0.382
Teacher spread0.254 · 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 designQualitative
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

Citations97
Published2015
Admission routes2
Has abstractyes

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