MétaCan
Menu
Back to cohort
Record W2123214683 · doi:10.1177/0093854810389534

A Comparison of Static and Dynamic Assessment of Sexual Offender Risk and Need in a Treatment Context

2010· article· en· W2123214683 on OpenAlexaff
Mark E. Olver, Stephen C. P. Wong

Bibliographic record

VenueCriminal Justice and Behavior · 2010
Typearticle
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsRecidivismRisk assessmentContext (archaeology)PsychologySex offenderPoison controlSex offenseInjury preventionPredictive validityHuman factors and ergonomicsClinical psychologyPsychiatryDemographySexual abuseMedicineComputer securityMedical emergencyComputer science

Abstract

fetched live from OpenAlex

The authors investigated the efficacy of static versus dynamic approaches to risk assessment and the validity of the Risk Principle through comparing treatment changes made by high- versus lower-risk offenders. The investigations were carried out using a sample of 321 treated sex offenders followed up for an average 10 years postrelease. Risk was assessed using the Static 99, and treatment change was assessed using the Violence Risk Scale—Sexual Offender version. Actuarially high-risk/ low-change offenders had significantly higher rates of sexual recidivism than similarly high-risk offenders who had demonstrated greater treatment changes. The Static 99 predicted sexual recidivism well among sex offenders with smaller treatment change but demonstrated weaker prediction among offenders with greater treatment change, likely owing, in part, to the static nature of the risk predictors. Implications regarding the dynamic nature of risk and potential utility of incorporating treatment change—related information into sex offender risk assessments are discussed.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.213
Threshold uncertainty score0.785

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 studies0.0000.000
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 head0.062
GPT teacher head0.426
Teacher spread0.364 · 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 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

Citations83
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueCriminal Justice and BehaviorSame topicPsychopathy, Forensic Psychiatry, Sexual OffendingFrench-language works237,207