Linking Culture and Language to Aboriginal Children’s Outcomes: Lessons from Canadian Data
Bibliographic record
Abstract
Aboriginal children have been shown to have poorer health and educational outcomes compared to non-Aboriginal children. Culture is an important determinant of health and well-being, yet it is rarely studied in terms of its association with young children’s outcomes. Language being one component of culture, the revitalization of traditional Aboriginal languages is an important contributor to both individual and community health as well as educational achievement. This paper will summarize multiple studies using data from the Aboriginal Children’s Survey and the Aboriginal Peoples Survey to highlight various outcomes for Aboriginal children in Canada, first in terms of the role of cultural participation, and then specifically speaking an Aboriginal language, on young Aboriginal children’s education and health outcomes. \n----- \nIl a été démontré ailleurs que les enfants autochtones sont en moins bonne santé et ont des résultats scolaires plus faibles relativement aux enfants non autochtones. La culture est un déterminant important de la santé et du bien-être, mais on ne l’étudie que rarement en fonction de son association aux résultats scolaires chez les jeunes enfants. La langue étant un des constituants de la culture, la revitalisation des langues autochtones traditionnelles est un contributeur important à la santé individuelle et communautaire ainsi qu’à la réussite scolaire. Cet article résume plusieurs études en utilisant les données de l’Enquête sur les enfants autochtones et l’Enquête auprès des peuples autochtones pour mettre en évidence divers résultats sur l’éducation et la santé pour les enfants autochtones au Canada – d’abord en termes du rôle de la participation culturelle, puis en ciblant ceux qui parlent une langue autochtone particulière.
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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 | 0.022 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.026 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".