“ē-nitomikoyahkik kakīwēyahk [They’re Calling Us Home]”: Kinship, Land, and Wellness in Indigenous Language Revitalization
Bibliographic record
Abstract
This paper examines the experiences of adult participants in the nēhiyawak Language Experience (nLE), a land-based Cree language immersion camp aimed at reclaiming nēhiyawēwin (Cree). Using Indigenous methodology, this study explores the following question: What are the experiences of adult learners and teachers in a land-based nēhiyawēwin immersion camp? The research design is based on a co-researcher model, where six participants (three learners and three teachers) engaged in a collaborative process of reflection and storytelling. Through three sharing circles held over a year, we shared stories of our camp experiences, then co-transcribed and analyzed these stories. Our study emphasizes relationship-building and nēhiyaw protocols as central to Indigenous knowledge generation. We address three key themes in this paper – kinship, land, and wellness – and demonstrate how these elements contribute to language learning and well-being. By situating the research within Indigenous epistemologies, this paper offers valuable insights into the role of land-based language camps in supporting language resurgence and fostering community wellness. It also provides recommendations for language practitioners, communities, and researchers committed to Indigenous language revitalization.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".