Exploring the Relationships of Perceived Discrimination, Anger, and Aggression among North American Indigenous Adolescents
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".