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Record W3109932718 · doi:10.1177/0093854820974400

Incremental Contributions of Static and Dynamic Sexual Violence Risk Assessment: Integrating Static-99R and VRS-SO Common Language Risk Levels

2020· article· en· W3109932718 on OpenAlexaff
Mark E. Olver, Sharon M. Kelley, Drew A. Kingston, Sarah M. Beggs Christofferson, David Thornton, Stephen C. P. Wong

Bibliographic record

VenueCriminal Justice and Behavior · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicSexual Assault and Victimization Studies
Canadian institutionsRoyal Ottawa Mental Health CentreUniversity of Saskatchewan
Fundersnot available
KeywordsRecidivismRisk assessmentPoison controlSexual violenceHuman factors and ergonomicsInjury preventionSuicide preventionPsychologyRisk analysis (engineering)MedicineClinical psychologyComputer securityComputer scienceMedical emergencyCriminology

Abstract

fetched live from OpenAlex

We examined the incremental contributions of static and dynamic sexual violence risk assessment in a multisite sample of 1,289 men treated for sexual offending. The study extends validation work that established new risk categories and recidivism estimates for the Violence Risk Scale–Sexual Offense version (VRS-SO), using the risk assessment common language (CL) framework. Different rates of sexual recidivism were observed at different thresholds of static risk (Static-99R) as a function of dynamic risk and treatment change, particularly for men who were actuarially above or well above average risk (Levels IVa and IVb, respectively). A framework integrating CL risk levels for Static-99R and VRS-SO dynamic scores into overall CL risk levels is presented. We discuss implications for dynamic sexual violence risk assessment regarding the language used for risk communication and the importance of dynamic risk instruments in sexual violence evaluations, particularly when credible agents of risk change may be present.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.408
Threshold uncertainty score0.808

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 studies0.0010.000
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 head0.044
GPT teacher head0.403
Teacher spread0.359 · 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

Citations15
Published2020
Admission routes1
Has abstractyes

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