Language: a key to resilience among Indigenous peoples
Bibliographic record
Abstract
Indigenous people continue to experience the negative effects of colonialism and related prejudice, discrimination, and racism. I investigated whether knowing an Indigenous language may protect Indigenous people from such harmful experiences. I hypothesized that Indigenous people who speak an Indigenous language would experience better mental health and that both belongingness and collective self-esteem would mediate this relationship. I used statistical mediation to assess these hypotheses with the Statistics Canada 2017 Aboriginal Peoples Survey (n = 19,509). Unexpectedly, knowing an Indigenous language had a significant and negative effect on mental health. Respondents who spoke an Indigenous language perceived their mental health as poorer. As hypothesized, knowing an Indigenous language had significant and positive indirect effects on mental health via both belongingness and collective self-esteem. Respondents who spoke an Indigenous language felt they belonged more and better about their Indigenous community. These results imply that learning an Indigenous language may be one effective “treatment” to improve Indigenous peoples’ mental health by fostering feelings of belongingness and collective self-esteem. The results are, however, based on correlational evidence among one-item measures, but it is not possible to ethically manipulate exposure to language or randomly assign people to learn a language. While acknowledging this design limitation, I explain the implications of these findings for language programming revitalization and mental health intervention.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".