American Communities and Schools
Bibliographic record
Abstract
This policy brief addresses the dual challenges facing Native American communities in their language planning and policy (LPP) efforts: maintaining heritage/community languages, and providing culturally responsive and empowering education. Using profiles of heritage-language immersion programs that have enabled Indigenous communities to reclaim their languages and incorporate local cultural knowledge in school curricula, it is clear that “additive ” or enrichment approaches are beneficial to students in such communities. These cases are significant because they show heritage-language immersion to be superior to English-only instruction even for students who enter school with limited proficiency in the heritage language. However, heritage-language immersion conflicts with the language policy of the federal No Child Left Behind Act of 2001, which provides no provisions for instruction or assessment in tribal or other non-English languages. Heritage language loss and shift toward English are occurring at an escalating pace in Indigenous communities throughout North America. Of 210 Native languagesstill spoken in the U.S. and Canada, only 34 (16 percent) are still being acquired as a first language by children. Unlike “world ” languages, such as Spanish, Indigenous 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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.025 | 0.005 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.011 | 0.008 |
| Insufficient payload (model declined to judge) | 0.081 | 0.006 |
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".