Indigenous Beadwork as a Method of Teaching Linear Algebra
Bibliographic record
Abstract
Abstract: In this work, the authors describe efforts aimed at Indigenizing a second- year linear algebra course at a small liberal arts university in Manitoba, Canada. This is done through an assignment, part hands- on and part written work, that explores the connection between Indigenous beadwork and linear algebra. Our collaboration was perhaps unconventional: Sarah, the first author, is a mathematics professor; while Cathy, the second author, is an associate professor in art history. However, we both had similar goals of putting theory into practice and making positive changes to student learning outcomes in a culturally appropriate way. We situate our work in the context of the current scholarly literature, adding to the important ongoing dialogue on Indigenization of course content and reflecting on the process and outcomes. This transformation of the course curriculum represented an applied approach to immerse Indigenous knowledge and pedagogy into a mathematics classroom. We hope that it may serve as an example of how other educators, particularly in science, technology, engineering, and mathematics (STEM), can integrate Indigenous knowledge- centered pedagogy into their classroom.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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.
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".