The Role of Cultural Instruction in Adult Indigenous Language Learning
Bibliographic record
Abstract
As one of the Calls to Action by the Truth and Reconciliation Commission (TRC) of Canada, the TRC demanded that the Canadian government fund language revitalization and preservation programs, including language courses at the postsecondary level. The demand for funding of language revitalization programs has, in turn, augmented the drive for Indigenous language revitalization (ILR) programs in communities and educational institutions, which results in a need for instructional programs. This article presents a review of the literature on Indigenous language instruction coupled with instruction in Indigenous cultural concepts among Indigenous adult learners of Anishinaabemowin: in particular, the focus is on learning gains when a component of cultural instruction is added to Indigenous language instruction to Indigenous adults. This review of literature highlights a gap in the research on adult Indigenous language learning and cultural teachings in the field of adult Indigenous language teaching. Additional research on this topic would contribute to the revitalization of Indigenous languages and contribute to fulfilling Calls to Action 13, 14, and 15 of the Truth and Reconciliation Committee. Addressing this gap in the research would advance knowledge about the role of cultural instruction in Indigenous language learning, which is useful for ILR curriculum planners and instructors.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".