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Record W1977234570 · doi:10.1177/1541204012469414

Youth Psychopathic Traits and Their Impact on Long-Term Criminal Offending Trajectories

2012· article· en· W1977234570 on OpenAlexaff
Heather L. Dyck, Mary Ann Campbell, Fred Schmidt, Julie L. Wershler

Bibliographic record

VenueYouth Violence and Juvenile Justice · 2012
Typearticle
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsLakehead UniversityThunder Bay Regional Health Sciences CentreUniversity of New Brunswick
Fundersnot available
KeywordsPsychopathyPsychologyTraitPsychopathy ChecklistAntisocial personality disorderInjury preventionJuvenile delinquencyPoison controlHuman factors and ergonomicsDevelopmental psychologyClinical psychologyPersonalitySocial psychologyMedicineMedical emergency

Abstract

fetched live from OpenAlex

The current study examined long-term offending patterns in relation to youth psychopathic traits. Criminal records of 126 adolescent offenders (80 male; 46 female) were analyzed for criminal activity between the ages of 12 and 23. Total scores on the Psychopathy Checklist: Youth Version were positively correlated with a higher number of overall offending incidents. After classifying youths into low ( n = 62), moderate ( n = 26), and high ( n = 38) psychopathic trait groups, results indicated that the moderate- and high-trait groups had consistently higher mean rates of criminal events (i.e., violent, nonviolent, drug related, and technical violations) throughout the follow-up period than the low-trait group. Contrary to what has been argued in previous psychopathy literature, a decrease in offending over time was observed in all three psychopathic trait groups. These results suggest that youths with psychopathic traits tend to display a higher level of criminal activity during adolescence, but are similar to lower psychopathic groups in also showing at least an initial decline in this behavior as they approach early adulthood.

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)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.587
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.048
GPT teacher head0.327
Teacher spread0.279 · 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

Citations27
Published2012
Admission routes1
Has abstractyes

Explore more

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