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Record W2137628040 · doi:10.1177/1079063213492340

Age, Actuarial Risk, and Long-Term Recidivism in a National Sample of Sex Offenders

2013· article· en· W2137628040 on OpenAlexaffabout
Terry P. Nicholaichuk, Mark E. Olver, Deqiang Gu, Stephen C. P. Wong

Bibliographic record

VenueSexual Abuse · 2013
Typearticle
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsMinistry of Community Safety and Correctional ServicesUniversity of Saskatchewan
Fundersnot available
KeywordsRecidivismDemographyCohortSex offenderPsychologyRisk assessmentSex offensePopulationPoison controlPsychiatryInjury preventionMedicineSexual abuseClinical psychologyMedical emergencyComputer securitySociology

Abstract

fetched live from OpenAlex

Age at release has become an increasing focus of study with regard to evaluating risk in the sex offender population and has been repeatedly shown to be an important component of the risk assessment equation. This study constitutes an extension of a study of sex offender outcomes prepared for the Evaluation Branch, Correctional Service of Canada. The entire cohort of 2,401 male federally incarcerated sexual offenders who reached their warrant expiry date (WED) within 1997/1998, 1998/1999, and 1999/2000 fiscal years were reviewed for the study. Sexual and violent reconviction information was obtained from CPIC criminal records over an average of 12.0 years (SD = 1.7) follow-up. This study focused upon the cohort of sex offenders who were 50 years or older at time of release (N = 542). They were stratified according to risk using a brief actuarial scale (BARS) comprising six binary variables. For the most part, older offenders showed low base rates of sexual recidivism regardless of the risk band into which they fell. The exception was a small group of elderly offenders (n = 20) who fell into the highest risk band, and who showed high levels of sexual recidivism. The results of this combination of cross-sectional and longitudinal analyses of elderly sexual offenders may have important implications for offender management, particularly in light of the increasing numbers of offenders in Canada who fall into the over 50 age cohort.

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 score0.196
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
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.040
GPT teacher head0.315
Teacher spread0.274 · 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

Citations34
Published2013
Admission routes2
Has abstractyes

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