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Record W7110924455 · doi:10.53967/cje-rce.7503

Divo Ti Vyin? Where Are You From? Red River Métis Knowledge as Pedagogy

2025· article· en· W7110924455 on OpenAlexaffvenueabout

Bibliographic record

VenueCanadian Journal of Education / Revue canadienne de l éducation · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous and Place-Based Education
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsIndigenousTraditional knowledgeHonourNarrativeColonialismIdentity (music)Indigenous education

Abstract

fetched live from OpenAlex

This article explores the integration of Red River Métis cultural identity into pedagogy, emphasizing the benefits for diverse students in the Canadian context. Using personal narratives and historical connections, it highlights the implications of land-based learning and critical historical analysis to infuse Indigenous perspectives into the elementary educational environment. The article investigates the historical and cultural roots of the Red River Métis, the impact of colonialism on their identity, and the role of Métis matriarchy in cultural continuity. Including Métis histories in the classroom is used to advocate for relational, land-based, and community-oriented pedagogies that honour Indigenous knowledge and promote reconciliatory education. The article also discusses the role of land acknowledgements and community-based events in creating inclusive connections to Indigenous epistemologies. By incorporating Métis pedagogies, educators can create inclusive, collaborative, and reflective learning experiences that support all students in understanding complex historical issues pertinent to their local geography and the broader Canadian society.

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)0.003
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.589
Threshold uncertainty score0.818

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0120.021
Scholarly communication0.0120.005
Open science0.0020.007
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.031
GPT teacher head0.324
Teacher spread0.293 · 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 designNot applicable
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 routes3
Has abstractyes

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