Learning as nēhiyaw: Understanding Language Learning with the NEȾOLṈEW Assessment Tool at the nēhiyawak Immersion Camp
Bibliographic record
Abstract
In the face of continuing pressure from colonial languages, many Indigenous languages are experiencing renewed growth through promising and essential practices such as immersion and land-based learning. This article illustrates the importance of a nēhiyaw (Cree) immersion camp steeped in nēhiyaw pedagogy and onto-epistemological underpinnings related to land. These beliefs embrace the nēhiyawak (Cree people) understanding that language is alive with spirit that enhances nēhiyaw identity. We engage in mixed methods research to explore the long-term witnessing of language learning on the land using the Indigenous-created NEȾOLṈEW̱ Assessment Tool and the lived experiences of those who attended the camp. This research was done with quantitative analyses of learners’ proficiency development during week-long immersion camps, as reflected by an assessment tool, over the course of 2 years and through qualitative feedback and reflections given by camp leaders, learners, and teachers. With this information, we reflect upon how the assessment tool can be adapted for specific use in Indigenous land-based language immersion settings. Additionally, we emphasize the criticality of immersive land-based learning and evaluation in giving strength to nēhiyawak, and indeed, all Indigenous languages across Turtle Island.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".