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Record W3043075527 · doi:10.1177/1541204020939643

Is the Foster Care-Crime Relationship a Consequence of Exposure? Examining Potential Moderating Factors

2020· article· en· W3043075527 on OpenAlexafffund
Jennifer Yang, Evan McCuish, Raymond R. Corrado

Bibliographic record

VenueYouth Violence and Juvenile Justice · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicChild Welfare and Adoption
Canadian institutionsSimon Fraser University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsFoster carePsychologySuicide preventionHuman factors and ergonomicsPoison controlInjury preventionCriminal justiceSubstance useJuvenile delinquencyEconomic JusticeDevelopmental psychologyClinical psychologyMedicineCriminologyMedical emergency

Abstract

fetched live from OpenAlex

Youth who are dually involved in both foster care and criminal justice systems represent a small minority of individuals with multi-problem risk profiles. Prior research has found that foster care youth are disproportionately more likely to be chronic offenders in both adolescence and emerging adulthood. However, the nature of this relationship remains theoretically underexplored and empirically underexamined, especially with respect to risk factors that may moderate the relationship. Using data from the Incarcerated Serious and Violent Young Offender Study, the criminal offending trajectories of 678 incarcerated youth were examined. A history of foster care predicted membership in a high rate chronic offending trajectory. This relationship was not moderated by parental maltreatment, negative self-identity, involvement in gang activity, or substance use versatility. Findings suggested a greater need for ongoing support for foster care youth during their transition to adulthood, regardless of their exposure to a range of other negative life circumstances.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.013
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.020
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.000

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 head0.077
GPT teacher head0.293
Teacher spread0.217 · 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 source (direct Gemma or distilled Codex), 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

Citations28
Published2020
Admission routes2
Has abstractyes

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