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Record W2737694780 · doi:10.1177/1079063216681563

Sexual Violence Risk Prediction in a Police Context

2016· article· en· W2737694780 on OpenAlexaff
Sandy Jung

Bibliographic record

VenueSexual Abuse · 2016
Typearticle
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsMacEwan University
Fundersnot available
KeywordsRecidivismPsychologySex offenseContext (archaeology)Sexual violenceDocumentationCriminologyHuman factors and ergonomicsPoison controlSuicide preventionRisk assessmentSexual abuseClinical psychologyComputer securityMedical emergencyMedicineComputer scienceGeography

Abstract

fetched live from OpenAlex

Adoption of evidence-based approaches by police services offers a practical and scientific solution to ensure public safety and proper allocation of resources. Advances in the field of sexual violence risk prediction have the potential to inform policing practices. The present study examines the validity of existing actuarial measures to predict the future sexual violence behavior of 290 identified male perpetrators of sexual assault against adult victims (ages 16 and older). The Static-99R and Static-2002R were coded from police documentation, and the sample was followed up for at least 1 year with an average of 3.6 years. Both measures showed large effects for predicting any offending, violent offending, and sexual offending in the form of charges and convictions. The findings suggest that existing sex offender research can extend to police practice, and criminogenic factors used to predict recidivism among convicted offenders may apply to assessing the risk posed by perpetrators of police-reported sexual assaults.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.367
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.003

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 head0.021
GPT teacher head0.291
Teacher spread0.270 · 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.

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

Citations2
Published2016
Admission routes1
Has abstractyes

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