Miscalculations: Decolonizing and Anti-Oppressive Discourses in Indigenous Mathematics Education
Bibliographic record
Abstract
In North American mathematics education, many practitioners highlight a disparity in achievement between Indigenous and non-Indigenous students, and claim that incorporating Indigenous perspectives in mathematics provides a more inclusive teaching approach. However, our analysis shows that there is a stream of North American practitioners who do not use anti-oppressive or decolonizing discourses, including those who claim to be motivated by social justice education. By avoiding or not emphasizing colonization, ongoing racism, and oppression in Indigenous mathematics education, these practitioners are perpetuating a false sense of the origins of inequality. Furthermore, the quest for Indigenous cultural connections in mathematics sometimes has consequences such as placing blame on Indigenous peoples for not being authorities on their cultures, perpetuating stereotypes, homogenizing Indigenous cultures while reducing their history and knowledge to superficial artifacts, and preserving a sense of the inferiority of Indigenous peoples when it comes to understanding and learning 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 | 0.021 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.024 | 0.065 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".