“Don’t Just Collect Words”: Strategies for Advanced Indigenous Language Learning
Bibliographic record
Abstract
Advanced adult Indigenous language speakers are essential in Indigenous language revitalization (ILR). As first language speakers age and pass away, communities increasingly depend on adults with high proficiency to carry the language forward (Fishman, 1991; Hinton, 2011; W.H. Wilson, 2018). Yet, few studies in ILR focus on adult learners, and fewer still on adults working on advanced proficiency. Similarly, in the field of applied linguistics (AL), minimal attention has been given to strategies for advanced language learning, and less still to Indigenous language learning (Daniels & Sterzuk, 2022; McIvor, 2020). This paper presents the results of a study aimed at understanding how adult Indigenous language learners have achieved advanced proficiency, including cases where there are few or no first language speakers to rely on for mentorship. It presents specific strategies and techniques that participants implemented to successfully progress to advanced proficiency. Insights from this context are shared to further understandings of, and possibilities for, greater connections between AL and ILR.
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.
How this classification was reachedexpand
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".