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Record W2103799661 · doi:10.1177/1079063211403503

Merging Developmental and Criminal Career Perspectives

2011· article· en· W2103799661 on OpenAlexaboutno aff
Jesse Cale, Patrick Lussier

Bibliographic record

VenueSexual Abuse · 2011
Typearticle
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsnot available
Fundersnot available
KeywordsRecidivismPsychologyRisk assessmentCriminal historyCriminal recordInjury preventionSuicide preventionHuman factors and ergonomicsPoison controlPsychiatryClinical psychologyDevelopmental psychologyCriminologyMedicineMedical emergencyComputer security

Abstract

fetched live from OpenAlex

Currently, a majority of actuarial risk-assessment tools for sexual recidivism contain static risk factors that measure various aspects of the offender's prior criminal history in adulthood. The goal of the current study was to assess the utility of extending static risk factors, by using developmental and criminal career parameters of offending, in the actuarial assessment of risk of violent/sexual recidivism. The current study was based on a sample of 204 convicted sexual aggressors of women incarcerated in the province of Quebec, Canada between April 1994 and June 2000. Semistructured interviews were used to gather information on the offender's antisocial history prior to adulthood, and police records were used to collect data on the criminal career of these offenders in adulthood. For an average follow-up period of approximately 4 years, the violent/sexual recidivism rate for the sample was 23.7%. The results provided support for the inclusion of both developmental and criminal career indicators for the prediction of violent/sexual recidivism. More specifically, recidivists were characterized by an early onset antisocial trajectory and a pattern of escalation of antisocial behavior between childhood and adolescence. The findings suggest that risk assessors should look beyond broad adult criminal history data to include aspects of antisocial development to improve predictive accuracy.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.172
Threshold uncertainty score1.000

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)0.0010.000

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 head0.079
GPT teacher head0.296
Teacher spread0.216 · 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 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

Citations14
Published2011
Admission routes1
Has abstractyes

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