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Record W7106020816 · doi:10.7939/83247

Sustaining Indigenous Language Growth in Elementary Schools: nēhiýawēwin Teachers and Assessment

2025· dissertation· en· W7106020816 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity of Alberta Library · 2025
Typedissertation
Languageen
FieldSocial Sciences
TopicMultilingual Education and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsNature versus nurturePopulationIndigenousLanguage acquisitionQualitative researchFirst languageProfessional developmentEarly childhoodOn Language

Abstract

fetched live from OpenAlex

This qualitative study explores teachers’ understandings of growth with children learning nēhiýawēwin for brief segments of time in elementary school contexts. The study draws upon nēhiýaw wisdom and constructivist philosophy to understand the experiences nēhiýawēwin teachers practicing in Canadian elementary classrooms. Three aspects of learning nēhiýawēwin at school are considered. First, how do nēhiýawēwin language teachers understand and experience language learning growth in schools, and what implications does this have for ongoing language learning? Second, what types of student activity, expression, and reports from other school staff and caregivers inform teachers’ perceptions of language growth, and how does this influence their instructional practice? Third, what implications do these experiences, perceptions, and understandings have for sustaining and inspiring future language learning? Demographic data shows that there has been a steady and growing trend toward the regular use of Ancestral languages in the homes of about half of the First Nations, Inuit, and Métis population (Norris, 2018). Although this is good news, it also indicates that the other half of First Nations, Inuit, and Métis population has limited exposure and opportunity to learn and experience their Ancestral languages. Teachers and schools across Canada occasionally attend to this disparity, sometimes at the community’s insistence and at other times with limited community support, by providing First Nations, Inuit, and Métis language learning opportunities for elementary children. The impact of these teaching and learning efforts on teachers and children is underexplored, particularly regarding language growth and the potential contribution to overall language reclamation. This study examines how teachers nurture and foster ongoing nēhiýawēwin learning in contested spaces. Historically, schools were used as tools to erase First Nations, Inuit, and Métis languages in Canada; however, contemporarily, they have become potential places of opportunity to learn those same languages. This study also examines the impact of contextual challenges, including time constraints and limited access to various learning resources (human, print, digital, experiential, and land-based), on language growth in the immediate context and beyond. There are significant gaps in the empirical research exploring the roles and practices of teachers who nurture and sustain First Nations, Inuit, and Métis languages in contested spaces, particularly when these are learned periodically and, as is commonly the case today, as a second, additional, or new Ancestral language. This research can potentially inform pedagogy and planning for future First Nations, Inuit, and Métis language development in schools. Preparing children to learn their Ancestral languages in ways that stimulate sustainability is integral to preserving and growing languages and supporting the well-being of First Nations, Inuit, and Métis people into the future.

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.830
Threshold uncertainty score0.338

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0150.006
Scholarly communication0.0040.003
Open science0.0020.006
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.014
GPT teacher head0.342
Teacher spread0.328 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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