From Generation to Generation: Survival and Maintenance of Canada's Aboriginal Languages, within Families, Communities and Cities
Bibliographic record
Abstract
The survival and maintenance of Aboriginal languages in Canada depend on their transmission from generation to generation. Children are the future speakers of a language. This paper demonstrates that the family and the community together play critical roles in the transmission of language from parent to child. On their own, neither family capacity nor community support is sufficient to ensure the adequate transmission of an Aboriginal language as a population's mother tongue from one generation to the next. Intergenerational transmission is maximized in Aboriginal communities among families where both parents have an Aboriginal mother tongue. Transmission can be best realized with the support of the community in those families with either both parents or the lone parent having an Aboriginal mother tongue. Outside of Aboriginal communities, particularly within large cities, transmission and continuity is significantly reduced even under ideal family conditions of linguistically endogamous parents. For exogamous families, it appears that community effect, while positive, is nevertheless limited in offsetting their low rate of mother tongue transmission. Trends indicate continuing declines in intergenerational transmission accompanied by a decreasing and ageing Aboriginal mother tongue population and a growing likelihood that Aboriginal languages will be learned increasingly as second languages.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".