From nomadic to static : issues of acculturation and resilience among First Nations youths
Bibliographic record
Abstract
The notions of risk, resilience, and acculturation were examined among a group of First Nations adolescents from a community in Northern Quebec. These adolescents are at high-risk for emotional and behavioural problems due to both their minority status and the remoteness of their locale. Accordingly, the research on risk and resilience in innercity youths was adapted to study this unique group of First Nations youths. The aims of the study were relevant to issues of adolescent wellness. The first aim was to identify the factors that help protect against the maladaptive outcomes associated with minority group status and living in a remote area. The protective factors included intelligence, ego development, and attachment. The second aim was to examine subtypes of acculturation and the differences between acculturation subtypes among these youths. The acculturation strategy of integration was expected to result in the best adaptation whereas that of marginalization was expected to lead to difficulties across the domains of social competence. The third aim was to examine competence over time, as problems in one domain tend to be related to later problems in other domains. A series of paper and pencil questionnaires were completed in classroom settings by 67 adolescents aged 11 to 19 years and their teachers. Specific patterns of resilience across domains of functioning were found. High levels of intelligence protected against diminished school performance but not against depressive symptomatology. Strong attachment relationships protected against depression and poor school performance. Acculturative strategy also protected against negative outcomes despite high stress living situation. However, positive outcome was not uniform across all domains of social competence, which is consistent with the notion of domain specificity of resilience. Not one individual who participated in this study, despite the benefit of protective factors, showed high levels of social competence across all domains. This argues for the inherent risk of living in a high stress neighbourhood. The information is relevant to furthering our understanding of First Nations youths and their families, and advances the literatures on risk and resilience in its application to a relatively unique community.
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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 | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".