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Record W2051013889 · doi:10.1177/2156869312441185

Exploring the Relationships of Perceived Discrimination, Anger, and Aggression among North American Indigenous Adolescents

2012· article· en· W2051013889 on OpenAlexaboutno aff
Kelley J. Sittner, Les B. Whitbeck, Dan R. Hoyt

Bibliographic record

VenueSociety and Mental Health · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicRacial and Ethnic Identity Research
Canadian institutionsnot available
FundersNational Institute on Drug AbuseNational Institute of Mental HealthNational Institute on Alcohol Abuse and Alcoholism
KeywordsAngerAggressionPsychologyJuvenile delinquencyIndigenousPoison controlDevelopmental psychologyPath analysis (statistics)Human factors and ergonomicsInjury preventionClinical psychologySocial psychologyMedicine

Abstract

fetched live from OpenAlex

A growing body of research has documented associations between discrimination, anger and delinquency, but the exact nature of these associations remains unclear. Specifically, do aggressive behaviors emerge over time as a consequence of perceived discrimination and anger? Or do adolescents who engage in aggressive behavior perceive that they are being discriminated against and become angry? We use autoregressive cross-lagged path analysis on a sample of 692 Indigenous adolescents (mean age=12 years) from the Northern Midwest and Canada to answer these research questions. Results showed that the direction of effects went only one way; both perceived discrimination and anger were significantly associated with subsequent aggression. Moreover, early discrimination and anger each had indirect effects on aggressive behavior three years later, and anger partially mediated the association between discrimination and aggression. Perceived discrimination is but one of many strains related to their unequal social position that these Indigenous youth experience, and have important implications for the proliferation of disparities in later life.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.184
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
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 head0.138
GPT teacher head0.377
Teacher spread0.239 · 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

Citations57
Published2012
Admission routes1
Has abstractyes

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