Supplementary materials for the article "Projected speaker numbers and dormancy risks of Canada’s Indigenous languages" published in <i>Royal Society Open Science</i>.
Bibliographic record
Abstract
UNESCO launched the International Decade of Indigenous Languages in 2022 to draw attention to the impending loss of nearly half of the world’s linguistic diversity. However, how the speaker numbers and dormancy risks of these languages will evolve remains largely unexplored. Here, we use Canadian census data and probabilistic population projection to estimate changes in speaker numbers and dormancy risks of 27 Indigenous languages. Our model suggests that speaker numbers could over the period 2001-–2101, decline by more than 90% in sixteen languages and that dormancy risks could surpass 50% among five. Since the declines are greater among already less commonly spoken languages, just nine languages could account for more than 99% of all Canadian Indigenous language speakers in 2101. Finally, dormancy risks tend to be higher among isolates and within specific language families, providing additional evidence about the uneven nature of language endangerment worldwide. Our approach further illustrates the magnitude of the crisis in linguistic diversity and suggests that demographic projection could be a useful tool in assessing the vitality of the world’s 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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.660 | 0.124 |
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".