Orthography Choice in Indigenous Language CALL Courses
Bibliographic record
Abstract
For many Indigenous Nations and organizations, computer-assisted language learning (CALL) courses have become an effective means to support Indigenous language revitalization and reclamation (ILR) efforts. Engaging a methodology of storywork and highlighting relationships between relevant fields of ILR, CALL, and applied linguistics, this article focuses on orthography choice and use in Indigenous language CALL courses. As contributors to three North American Indigenous language courses—Chikashshanompa' (Chickasaw) on Rosetta Stone, Kwak̓wala on 7000 Languages, and Southern Michif for Beginners on 7000 Languages, we offer reflections on community-led processes which addressed tensions and challenges in representing written language in CALL courses. Through reflections, we illuminate the complexity of orthography choice and use in Indigenous language CALL courses and share strategies with others creating their own Indigenous language courses.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".