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Record W2767304037 · doi:10.1177/0011128717741616

Do Risk and Protective Factors for Chronic Offending Vary Across Indigenous and White Youth Followed Prospectively Through Full Adulthood?

2017· article· en· W2767304037 on OpenAlexafffund
Evan McCuish, Raymond R. Corrado

Bibliographic record

VenueCrime & Delinquency · 2017
Typearticle
Languageen
FieldPsychology
TopicChild Abuse and Trauma
Canadian institutionsSimon Fraser University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsIndigenousSocioeconomic statusDemographyWhite (mutation)Poison controlSuicide preventionInjury preventionHuman factors and ergonomicsPsychologyCriminologyGerontologyMedicineEnvironmental healthSociologyPopulation

Abstract

fetched live from OpenAlex

Although Indigenous youth are overrepresented in justice systems across North America, Australia, and New Zealand, explanations for this overrepresentation are principally theoretical as data at the individual level are lacking. Risk for offending among Indigenous youth may be overestimated because of their typically more negative socioeconomic outcomes tied to historical injustices perpetrated by governments across different nations. Data on 403 adolescent offenders followed from ages 12 to 29 were used to examine offending trajectories and associated risk and protective factors across Indigenous and White participants. A greater number of social adversities characterized Indigenous youth, yet they did not differ from White youth in their likelihood of assignment to the highest rate offending trajectory. Culturally sensitive assessment of risk for offending is recommended.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.053
Threshold uncertainty score0.105

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.057
GPT teacher head0.352
Teacher spread0.295 · 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

Citations11
Published2017
Admission routes2
Has abstractyes

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