Indigenous Language Revitalization and Applied Linguistics: Parallel Histories, Shared Futures?
Bibliographic record
Abstract
Abstract Damages done to Indigenous languages occurred due to colonial forces, some of which continue to this day, and many believe efforts to revive them should involve more than Indigenous peoples alone. Therefore, the need for learning Indigenous languages as “additional” languages is a relatively new societal phenomenon and Indigenous language revitalization (ILR) an emerging academic field of study. As the ILR body of literature has developed, it has become clear that this work does not fit neatly into any single academic discipline. While there have been substantial contributions from linguistics and education, the study and recovery of Indigenous languages are necessarily self-determined and self-governing. Also, due to the unique set of circumstances, contexts, and, therefore, solutions needed, it is argued that this discipline is separate from, yet connected to, others. Applied linguists hold specific knowledge and skills that could be extended to ILR toward great gains. This paper explores current foci within ILR, especially concepts, theories, and areas of study that connect applied linguistics and Indigenous language learning. The intention of this paper is to consider commonalities, differences, current and future interests for shared consideration of the potential of collaborations, and partnerships between applied linguistics and ILR scholars.
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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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.043 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.004 |
| 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".