Indigenous Language Revitalization and Applied Linguistics: Parallel Histories, Shared Futures?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it