Progress, challenges, and trajectories for indigenous language content-based instruction in the United States and Canada
Bibliographic record
Abstract
Abstract Indigenous language content-based instruction in the United States and Canada is primarily known as Indigenous language medium or Indigenous language immersion (ILI) education. In spite of huge barriers, it has grown over the past decade. Programs have emerged from concerns about language loss and a desire for language revitalization. Language revitalization takes several generations since it seeks an outcome where the Indigenous language is primary with high, but secondary, proficiency in the nationally dominant language. To establish a trajectory to reach such an outcome, the majority of schooling until high school graduation should be through the Indigenous language. Indigenous language medium schooling also seeks to produce sufficient mastery of academics and English for access to English medium higher education. Where a sufficiently strong model has been implemented, as in Hawaiʻi, those results are beginning to be produced. At present, the models being implemented elsewhere in the two countries are at varying stages of development, with minimal government support.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.003 | 0.004 |
| 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".