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Record W2065867097 · doi:10.1177/1079063211403162

Dynamic Risk Groups Among Adult Male Sexual Offenders

2011· article· en· W2065867097 on OpenAlexaff
Michael C. Seto, Yolanda M. Fernandez

Bibliographic record

VenueSexual Abuse · 2011
Typearticle
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsMinistry of Community Safety and Correctional ServicesRoyal Ottawa Mental Health Centre
Fundersnot available
KeywordsPsychologyClinical psychologyDevelopmental psychologyDemography

Abstract

fetched live from OpenAlex

In the present study, the 16-item Stable-2000 was used to identify different dynamic risk groups among 419 adult male sexual offenders who were referred for assessments between 2000 and 2007. Using a two-stage cluster analysis, four dynamic risk groups were identified: (a) a low needs group who scored below the overall sample mean on all of the Stable-2000 items; (b) a typical group who had intermediate scores on many items; (c) a sexually deviant group who scored relatively high on deviant sexual interests, sexual preoccupation, emotional identification with children, and child molester attitudes; and (d) a pervasive high-needs group who scored relatively high on many Stable-2000 items, reflecting a variety of problems in both general and sexual self-regulation. These dynamic risk groups were not redundant with offender type based on victim age, relatedness, or gender, and did not differ in terms of age at time of assessment, marital status, number of sexual victims, or long-term risk, estimated using the Static-99. The implications for treating and supervising sexual offenders with different dynamic risk profiles 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)0.001
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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.294
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.004

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 head0.027
GPT teacher head0.270
Teacher spread0.243 · 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; both teacher heads agree on what is shown here.

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

Citations24
Published2011
Admission routes1
Has abstractyes

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