Perspectives and practices of Asian Canadian teachers in decolonizing mathematics and music education
Bibliographic record
Abstract
This thesis is a research study of the practices and perspectives of six Asian Canadian teachers as non-Indigenous settlers of colour in decolonizing education in the learning areas of mathematics and music. With a growing population of migrant communities, I raise the importance of understanding how people of colour construct their racial, national, cultural and settler identities as Canadians including their relationships with Indigenous peoples, lands, and knowledge systems. With the background of my own experience as an Asian New Zealander, I explore how Asian Canadian teachers have been practicing Indigenization and decolonization in their pedagogy. Drawing upon scholars in Indigenous, settler, and Asian Critical Studies, I investigate how participants’ constructs of identity affect their sense of responsibilities to participate in Indigenizing and decolonizing their teaching practice. Data were collected through semi-structured interviews with six Asian Canadian teachers—three mathematics teachers and three music teachers. The interviews explored participants’ life stories and experiences that contributed to their identity construction as Asian Canadians and their experiences in learning and teaching Indigenous knowledge and worldviews. The findings suggest that the participants face experiences of being perpetual foreigners/denizens which I theorize is a barrier to Asian teachers realising responsibilities to decolonize. I offer my suggestions for stakeholders in education – i.e., policymakers, administrations, and educators – in the form of various approaches to decolonize education that centre goals of Indigenous self-determination.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.038 | 0.021 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".