Bibliographic record
Abstract
miskâsowin askîhk is a nêhiyawêwin word that translates roughly “as finding oneself on the land.” Throughout this paper, I aim to tell a story about the journey I have taken on the land, with the language. The paper also addresses a process of coming to find myself throughout these experiences and relationships with land and language. Through my stories on the land, I have learned that I belong to the land and that the land teaches me. The article also shares what I have learned from Elders, Knowledge Keepers and literature. Namely, learning language on the land, with the land's resources, is an effective way to revitalize language and reclaim Indigenous identity in a balanced way. I finish this paper with the description of a project that I would like to research further. The project involves hand making beaded leather mitts while learning to speak nêhiyawêwin. This project is connected to asōnamēkēwin, a word in nêhiyawêwin that means that it is our responsibility to pass on knowledge that we learn. This is another important nêhiyawêwin phrase that guides me on this journey. It is my responsibility and I pass this responsibility onto anybody that I teach, to teach what they learn. Keywords: land-based learning, Cree language learning, language revitalization, best practices
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.013 | 0.009 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".