Respect, Responsibility, Relevance, and Reciprocity: What the 4 Rs of Indigenous Research Offer Toward Decolonizing a Mathematics Classroom
Bibliographic record
Abstract
Working to decolonize one’s mathematics teaching practice can create tensions between honouring Indigenous ways of knowing and being and not appropriating or tokenizing Indigenous cultures. This paper describes a mathematics teacher’s path towards decolonization in her grade 6/7 classroom in Saskatchewan, Canada. Through self-study research, Giroux created a framework using the 4 Rs of Indigenous research (respect, responsibility, relevance, and reciprocity), posing the research question: What is the value of my 4 Rs pedagogical framework for my professional growth as I aim to disrupt power and control in my mathematics classroom? Data, collected through a research journal, critical friend interviews, and student work, were examined using thematic analyses. Findings point to several semantic and latent themes of noticeable importance in disrupting power and control, while strengthening relationships, within the classroom. In this paper, the themes are presented and discussed in the context of decolonizing the mathematics classroom and for grounding the teaching and learning of mathematics in Indigenous perspectives and pedagogies based in the 4 Rs framework. Implications of the research include possibilities for K-12 educators to embrace and engage with Indigenous perspectives toward disrupting traditional power norms, promoting student agency, and strengthening relationships in the classroom and beyond.
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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 | 0.015 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.069 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| 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".