Sharing Indigenous Knowledges in University Teaching: The Need for Conciliable Spaces
Bibliographic record
Abstract
Calls to Indigenize the curriculum have been occurring and, indeed, increasing across Canadian universities since the release in 2015 of the calls to action of the Truth and Reconciliation Commission (TRC, 2015). The present article documents the emergence at two universities of a support program for Indigenous curriculum, in the form of digital Indigenous Learning Bundles. The Indigenous Learning Bundles model was first conceptualized in 2018 by Kanien’kehá:ka (Mohawk) scholar Kahente Horn-Miller of Carleton University, in Ottawa, Canada, and was later adopted in 2021 by an Indigenous-led curriculum development team at Western University. Both projects involve deep collaborations between Indigenous and non-Indigenous people and strive to unite people around the ethical inclusion of Indigenous knowledge in a post-secondary setting. The bundles involve the co-creation of a collection of Indigenous-led digital teaching resources that prioritize local Indigenous knowledges and ethics in the making and delivering of learning opportunities in classrooms. The present paper draws on Indigenous approaches to scholarship in teaching and learning to document the development of these unique Indigenous Learning Bundles. Using aspects of case study and self-study research, the authors review and analyze project documents and their own experiences of the project to offer up six core tenets of Indigenous Learning Bundles work. They suggest that such work should uphold Indigenous ethics and intellectual sovereignty; privilege local Indigenous community voices and knowledges; operate in conciliable spaces outside Euro-Western academic governance and disciplinary structures; engage people collaboratively in the development process; rely on ongoing instructor supports to facilitate the teaching in classrooms; and require ongoing institutional support to be sustained into the future. The authors discuss the strengths and limitations of Indigenous bundles work in general and make recommendations for educators and universities wishing to explore similar Indigenous curriculum projects.
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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 | 0.028 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.032 | 0.080 |
| Scholarly communication | 0.039 | 0.032 |
| Open science | 0.004 | 0.037 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
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".