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Record W4389252400 · doi:10.20429/ijsotl.2023.17205

Non-Indigenous Instructors Teaching about Indigenous Content: Reflections and Recommendations from Indigenous Ways of Knowing and Pedagogy

2023· article· en· W4389252400 on OpenAlexaff
Manu Sharma, Peggy Shannon‐Baker

Bibliographic record

VenueInternational Journal for the Scholarship of Teaching and Learning · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicEducator Training and Historical Pedagogy
Canadian institutionsThompson Rivers University
Fundersnot available
KeywordsIndigenousPedagogyScholarshipContext (archaeology)Teaching methodSociologyMulticulturalismEquity (law)Traditional knowledgePolitical scienceGeographyEcology

Abstract

fetched live from OpenAlex

This article takes a scholarship of teaching and learning approach to improve the authors teaching about Indige-nous content as non-Indigenous teacher educators. It explores how they attempted to incorporate Indigenous content and teaching practices into multicultural education classes and then reflect on how they could have improved their teaching practice. Both authors provide their unique positionality which provides context which is essential to consider when doing equity-based work such as teaching about/with Indigenous communities. The authors discuss their teaching experiences after they occurred with one another and then engage in an exploration via literature on teaching about Indigenous content. The outcomes of two years of co-reflection and analysis of the literature are shared in this article in hopes to help guide both the authors and other non-Indigenous instructors on how to improve their teaching and learning about Indigenous content in courses. The findings stress the importance of (1) acknowledging land as a conduit for domination, (2) recognizing all who teach us, and (3) Indigenous guest lecturers and intergenerational learning.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.226
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0060.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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 head0.178
GPT teacher head0.444
Teacher spread0.266 · 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 teacher head, not a consensus.

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

Citations1
Published2023
Admission routes1
Has abstractyes

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