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Sexual Offender Recidivism Risk

2003· article· en· W1590196730 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAnnals of the New York Academy of Sciences · 2003
Typearticle
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsGovernment of Canada
Fundersnot available
KeywordsRecidivismPsychologyRisk assessmentSex offenderSex offenseRisk management toolsActuarial scienceClinical psychologyDemographyHuman factors and ergonomicsPoison controlSexual abuseMedicineMedical emergencySociologyComputer securityComputer scienceBusiness

Abstract

fetched live from OpenAlex

If all sexual offenders are dangerous, why bother assessing their risk to reoffend? Follow-up studies, however, typically find sexual recidivism rates of 10%-15% after five years, 20% after 10 years, and 30%-40% after 20 years. The observed rates underestimate the actual rates because not all offences are detected; however, the available research does not support the popular notion that sexual offenders inevitably reoffend. Some sexual offenders are more dangerous than others. Much is known about the static, historical factors associated with increased recidivism risk (e.g., prior offences, age, and relationship to victims). Less is known about the offender characteristics that need to change in order to reduce that risk. There has been considerable research in recent years demonstrating that structured risk assessments are more accurate than unstructured clinical assessments. Nevertheless, the limitations of actuarial risk assessments are sufficient that experts have yet to reach consensus on the best methods for combining risk factors into an overall evaluation.

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.

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)0.003
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.469
Threshold uncertainty score0.693

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0020.000
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 head0.152
GPT teacher head0.383
Teacher spread0.231 · 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