Aggressive delinquency among north American indigenous adolescents: Trajectories and predictors
Bibliographic record
Abstract
Aggressive delinquency is a salient social problem for many North American Indigenous (American Indian, Canadian First Nations) communities, and can have deleterious consequences later in life. Yet there is a paucity of research on Indigenous delinquency. Group-based trajectory modeling is used to prospectively examine trajectories of aggressive delinquency over the course of adolescence using data from 646 Indigenous adolescents from a single culture, spanning the ages of 10-19. Five aggression trajectory groups were identified, characterized by different levels and ages of onset and desistence: non-offenders (22.1%), moderate desistors (19.9%), adolescent-limited offenders (22.2%), high desistors (16.7%), and chronic offenders (19.2%). Using the social development model of antisocial behavior, we selected relevant risk and protective factors predicted to discriminate among those most and least likely to engage in more aggressive behavior. Higher levels of risk (i.e., parent rejection, delinquent peers, substance use, and early dating) in early adolescence were associated with being in the two groups with the highest levels of aggressive delinquency. Positive school adjustment, the only significant protective factor, was associated with being in the lowest aggression trajectory groups. The results provide important information that could be used in developing prevention and intervention programs, particularly regarding vulnerable ages as well as malleable risk factors. Identifying those youth most at risk of engaging in higher levels of aggression may be key to preventing delinquency and reducing the over-representation of Indigenous youth in the justice system.
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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 | 0.001 |
| 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".