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Record W329794053 · doi:10.11575/prism/32512

Self-Reported Delinquency Among Alberta's Youth: Findings from a Survey of 2,001 Junior and Senior High School Students

2003· article· en· W329794053 on OpenAlexaffabout
Jeanette T Gomes

Bibliographic record

VenuePubMed · 2003
Typearticle
Languageen
FieldSocial Sciences
TopicYouth Development and Social Support
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsJuvenile delinquencyPsychologyPsychosocialPoison controlInjury preventionHuman factors and ergonomicsSuicide preventionCriminologyDevelopmental psychologyClinical psychologyPsychiatryMedicineMedical emergency

Abstract

fetched live from OpenAlex

This article draws on data from a 1999 survey on youth victimization, crime and delinquency in Alberta conducted by the Canadian Research Institute for Law and the Family in collaboration with researchers from the University of Alberta. The survey included 2,001 youth attending Grades 7 to 12 in public and Catholic schools in selected urban and rural areas in the province. Analyses focus on self-reported past-year delinquency. Statistically significant results were found for relationships between extent of delinquency and gender, grade level, psychosocial problems (as measured by conduct, hyperactivity, and emotional problems), and extent of past-year victimization. For low/moderate delinquency, females were comparable to males, and even reported slightly higher rates for low/moderate violence-related delinquency. Younger students were more likely to indicate engaging in violence-related delinquency, while older students were more likely to report property-related delinquent acts. Overall, Grade 9 students had the highest rates of delinquency. For personal characteristics, a high score on conduct problems was most strongly correlated with moderate/high delinquency. The relationship between high levels of delinquency and victimization was stronger for violence-related delinquency than for property-related delinquency.

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)0.003
metaresearch head score (Gemma)0.003
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 score0.127
Threshold uncertainty score0.950

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.034
GPT teacher head0.270
Teacher spread0.236 · 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

Citations13
Published2003
Admission routes2
Has abstractyes

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