Culturally Responsive Pedagogy and Mathematics Through Creative and Body-Based Learning: Urban Aboriginal Schooling
Bibliographic record
Abstract
Global neoliberal imperatives that numerically measure student success through standardized testing undermine the educational outcomes of students, in particular Indigenous students, and construct a seemingly fixed reality that avoids State responsibility to address structural inequality in Australia. Achievement gaps between Indigenous and non-Indigenous school students in mathematics have become an urgent international problem. Although evidence suggests that culturally responsive pedagogies (CRPs) improve student academic success for First Nations peoples in settler colonial countries such as the United States, Canada, New Zealand, and Australia, less prominent is a focus on how CRP is enacted and mobilized in Australian classrooms. Although some initiatives exist, this article explores how creative and body-based learning (CBL) strategies might be utilized to enact CRP. Using an ethnographic case study approach, we examined how two early career teachers serving Indigenous and ethnically diverse students implemented CBL to reengage students with mathematics. Findings suggest that the teachers were able to mobilize a number of CRP principles using CBL strategies to facilitate engagement in mathematics for urban Aboriginal students. Specifically, when teachers repositioned students as “competent” and designed embodied learning experiences that connected to their cultural backgrounds, students let go of their cautious learner histories and remade themselves as clever and competent.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| 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".