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Record W2298017939 · doi:10.1080/0735648x.2015.1105147

Risk profiles and serious antisocial behaviors of incarcerated children in care

2015· article· en· W2298017939 on OpenAlexafffund
Lauren F. Freedman, Jennifer S. Wong, Raymond R. Corrado

Bibliographic record

VenueJournal of Crime and Justice · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicChild Welfare and Adoption
Canadian institutionsSimon Fraser University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPsychologyAntisocial personality disorderClinical psychologyDevelopmental psychologyPsychiatryMedicineInjury preventionMedical emergencyPoison control

Abstract

fetched live from OpenAlex

Children in care (CIC) are at a heightened risk of engaging in serious and violent offending. This has been explained in relation to cumulative risk, whereby CIC are more likely to be exposed to traditional criminogenic risk factors, in addition to those specific to placement in care (e.g. removal from parental custody, placement shifts among caregivers). Limited research has been conducted on CIC engaging in serious and violent offenses, and thus it remains unclear whether these youth are at an increased risk of engaging in antisocial behavior when compared to other young offenders who are also exposed to a multitude of risk factors. Using a sample of 417 male-incarcerated youth in British Columbia, CIC and non-CIC were compared across a range of risk factors and antisocial behaviors to ascertain whether CIC represent a distinct population with unique risks and needs compared to their non-CIC counterparts. Though several risk factors were observed at a high frequency among CIC participants, few significant differences were observed in bivariate or multivariate analyses.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.114
Threshold uncertainty score0.327

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)0.0000.000

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 head0.015
GPT teacher head0.309
Teacher spread0.294 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2015
Admission routes2
Has abstractyes

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