Nahkawēwin Revitalization: A Mini Language Nest Created With Hope and Determination.
Bibliographic record
Abstract
This research based on my master's thesis explores Nahkawēwin language revitalization. This study draws on the language nest model, which first originated with Maori grandmothers and their grandchildren in the 1970s. In this study, my mother and I created what I refer to as a "mini" language nest in both of our homes to teach my children Nahkawēwin in a holistic manner. I call this a "mini" language nest because our nest only involved myself, my mother, and my children, when other language nests around the world have had multiple grandmothers and children who are participants of the language nest. This article aims to show how this approach to language nests can be used to revitalize or revive a language using intergeneration learning and teaching. In this study, I reflect on the different challenges one may face while creating a mini language nest, and how one might overcome these challenges through different language strategies, frameworks, and teaching tools. I do not wish to present language nests as a foolproof solution; rather, I share the reality of how one thought or intention can change the outcome of language learning in a positive manner. The language nest did not only teach my children their language, it brought us together with compassion, enthusiasm, and hope. Keywords: Indigenous, language, revitalization, revival, language nest, linguistic landscape, intergenerational learning.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".