Longitudinal Mechanisms Linking Perceived Racial Discrimination to Aggressive Delinquency among North American Indigenous Youth
Bibliographic record
Abstract
OBJECTIVES: Drawing from an integrated general strain theory framework, the purpose of the study is to examine the longitudinal mediating and moderating mechanisms linking perceived racial discrimination with aggressive delinquency among North American Indigenous (i.e., American Indian and Canadian First Nations) youth. METHODS: Data come from an eight-year longitudinal study of Indigenous youth residing on reservations/reserves in the upper-Midwest and Canada (N = 659). Scales were created for discrimination, depressive symptoms, school bonds, and delinquent peer associations at years 2 and 3, and a count measure of aggression was created at years 2, 3, and 5. Cross-lagged path analysis models were estimated to examine possible mediating effects of depressive symptoms, school bonds, and delinquent peer associations. Separate regression models were examined to test for possible moderating effects of the aforementioned variables. RESULTS: The results of a longitudinal path analysis model showed that discrimination indirectly increased aggression through decreased school bonds and increased delinquent peer associations. Depressive symptoms was the only significant moderator, and contrary to expectations, the effect of discrimination on aggression declined in magnitude as depressive symptoms increased. CONCLUSIONS: Discrimination is a key criminogenic stressor among Indigenous youth and is linked with multiple adverse outcomes through the adolescent years.
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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 | 0.001 | 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) | 0.002 | 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".