Ē-pī-wīcihtāsowin ahpō ē-pī-wīchisowin: non-indigenous learners in Indigenous language-learning spaces
Bibliographic record
Abstract
This paper contributes to ongoing conversations on the contextual differences and considerations between learning an Indigenous language as a member of an Indigenous nation or community and learning an Indigenous language as a non-Indigenous person (Albury, 2015; Berardi-Wiltshire & Bortolotto, 2022; May 2023; O’Toole, 2020; Te Huia, 2020). While we see value in considering how non-Indigenous Canadians can positively contribute to Indigenous language revitalization efforts, we also want to consider the consequences of that involvement for Indigenous Peoples who are asked to share Indigenous language learning spaces with non-Indigenous students. As a group of colleagues from different communities, all with connections to Indigenous language revitalization, we came together to consider questions such as: Are Indigenous languages for everyone or are they languages that should be learned only by Indigenous peoples? And if we accept or encourage Indigenous language learning by non-Indigenous Canadians, are there parameters that might need to be implemented? To this end, we used the Indigenous research method of conversation (Kovach, 2010) during biweekly meetings recorded on Zoom. Data consisted of meeting transcripts and web-based documents of written reflections. In our analysis of these documents, we identified three interrelated themes: 1) linguistic insecurity, 2) trauma and language learning and 3) settler dominance in Indigenous language settings. As a settler colonial country, Canada’s past and present continues to shape interactions between Indigenous Peoples and non-Indigenous Canadians in ways relevant to the topic of Indigenous language revitalization. Ultimately, we do want non-Indigenous people to learn Indigenous languages so that these languages can once again be languages of broader society (McIvor, 2012). We also recognize that Indigenous Peoples deserve to reclaim Indigenous languages in safe and trauma-free ways. Ultimately, we must work together to ensure that including non-Indigenous learners in Indigenous language programs does not cause injury to Indigenous learners. This paper offers recommendations for ways to achieve these goals.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.049 | 0.007 |
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".