Task-Based Teaching of Indigenous Languages: Investment and Methodological Principles in Macuiltianguis Zapotec and Salish Qlispe Revitalization
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
Task-based language teaching (TBLT) is a pedagogical approach that involves identifying real-world tasks that learners need to be able to do in the target language and then developing classroom-appropriate, context-specific versions of these tasks. In this paper, we use Long’s methodological principles for TBLT to evaluate a task-based approach within two Indigenous language-teaching contexts: the Macuiltianguis Zapotec classroom in Oaxaca, Mexico, and a workshop for teachers of Salish Qlipse in the state of Montana. Throughout the article, we give special consideration to issues of investment in the target language, expanding on Norton’s definition of language learner investment to argue that teacher and community investment in the language and language revitalization process are critical to the successful implementation of TBLT in Indigenous contexts.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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