Narratives Indigenizing School Mathematics: An Intersection of Euro-Western and Cree Perspectives
Bibliographic record
Abstract
Abstract My research is a personal effort to understand the experiences that have shaped my work, practice, and living of teaching mathematics. From the boy storied as being smart in mathematics to the man who was tasked in finding ways to Indigenize school mathematics, I have composed stories to live by that share the tensions, conflicting stories, and mis-educative experiences that have shaped who I am as a White Euro-Western mathematician in a Canadian prairie province. My research wonder serves a practical justification as I “attend to the importance of considering the possibility of shifting, or changing practice” (Clandinin, 2013, p. 36) in the context of cross-cultural teaching and learning. Much of the research around Indigenous mathematics education is shaped by misconceptions of Indigenization and inconsistent practices of how this is taken up by practitioners – topics that I analyzed during my doctoral studies. Through my inquiry described in the chapter, I hoped to achieve a nuanced understanding of how the experiences of diverse lives shape the learning of school mathematics.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.018 | 0.062 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".