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Record W7064062104

An Autoethnography of Cree Language Learning

2025· article· en· W7064062104 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity Library (University of Saskatchewan) · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdaptive optics and wavefront sensing
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousVitalityAutoethnographyIndigenous languageColonialismLanguage revitalization
DOInot available

Abstract

fetched live from OpenAlex

Canada’s damaging colonial legacies of the residential schools and its coercive colonial policies resulted in the gargantuan loss of Indigenous languages, the brokenness of families, and the intergenerational trauma faced by Indigenous People today. The most challenging task remains reteaching Indigenous languages in communities whose languages are endangered or at risk of being permanently lost. Canada supports Indigenous languages; however, despite Canada’s financial support, it takes a community to teach a child their Indigenous language; it starts at home, at the community level, and it does not fall on the family alone nor the school rather it is a community task. It starts at home in the community, utilizing the vitality of Indigenous languages as a means of cultural sustainability integral in community sustainable development. The nine pillars of sustainability: environmental, health, social, economic, and cultural (Tsuji, 2021, p. 2, Tsuji, 2022, p. 8). Indigenous language maintenance, education, governance, and leadership are all integral components of cultural sustainability in Indigenous communities. Cultural teachings and the vitality of Indigenous languages are interwoven into the pillars of sustainability. Too often, we overlook the vitality of Indigenous languages at the community level and its integral role in Indigenous language maintenance. I examine the role of vitality in my stories on how I learned Cree as my first language and make cultural connections to our sustainability. Based on my knowledge, I share how we can teach Indigenous languages at the community level. My autoethnography combines Western and Indigenous research frameworks, first language acquisition theories, I apply theories and studies learned in educational administration leadership in demonstrating partnerships with educational institutions and communities bear fruit in working together to ensure maintenance of Indigenous languages. I weaved in threads of Indigenous ways of knowing and contexts, comparative and international contexts, challenges for education, social justice and equity and ethical leadership to include all stakeholders in Indigenous languages. I chose my community, the Red Earth Cree Nations, as an exemplary reserve in demonstrating the community and school on working together to ensure maintenance of our Indigenous language. My findings demonstrate the critical role of the family and the vitality of Indigenous languages, the close relationship of cultural sustainability and environmentalism in our sustainability, as a family and at the community level are key factors in Indigenous languages maintenance. Further analysis of the findings led to a window of great opportunity to share and contribute to the research of Indigenous first language acquisition and Cree language development. There is a need for more research on Indigenous first language acquisition, vitality in Indigenous languages, cultural sustainability, the relationship of cultural sustainability and environmentalism in Indigenous community sustainable development. I honor my dad, Late Herman Garvin who taught me the integral role of our Cree language when he taught us that our livelihood is the land, our cultural sustainability, and environmentalism, to respect Mother Earth, I created and present the Herman Garvin Cultural Sustainability Approach. This approach is founded on the belief that cultural sustainability is the main principle, and vitality is the key to successful Indigenous language maintenance.

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)0.005
metaresearch head score (Gemma)0.012
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 score0.966
Threshold uncertainty score0.070

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.012
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0160.018
Scholarly communication0.0070.005
Open science0.0020.007
Research integrity0.0030.007
Insufficient payload (model declined to judge)0.0090.002

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 head0.005
GPT teacher head0.181
Teacher spread0.176 · 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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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