Large Language Model Translation of Indigenous Languages
Bibliographic record
Abstract
The territory known as Canada is home to an incredible diversity of Indigenous languages. Before settler contact, it was estimated that there were anywhere between 300 and 450 Indigenous languages and dialects, belonging to 11 language families [1] . These languages were distinct in nature, with unique properties and dialects that spanned from coast to coast. In current times, the diversity of Indigenous languages in Canada has dropped dramatically, with census data from 2021 revealing only around 70 Indigenous languages remain spoken today, and the number of Indigenous people that could speak an Indigenous language at a conversational level has declined by 4.3% from 2016 [2] . Of these languages, approximately 57% have fewer than 500 active speakers [2] , indicating the desperate need for intervention to preserve and revitalize these languages. This decline of Indigenous language transmission in Canada can be attributed to historic, and ongoing systemic factors related to colonization, such as residential schools and the Indian Act [3] . Despite over a century and a half of oppressive governmental policies aimed at destroying Indigenous languages, Indigenous communities are dedicated to revitalizing and reclaiming their language and culture [3] .
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.009 |
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".