"I'm too White to teach Native History": Unpacking educator resistance to teaching Aboriginal pedagogy
Bibliographic record
Abstract
The Indigenous people of Canada are worthy of special focus as the original people of this continent. However, current research shows that there continues to be a persistent lack of integration of Indigenous content, perspectives and ways of knowing in schools. This paper presents original research findings on how teachers infuse Aboriginal history and culture into their curriculum, with a focus on why teachers may hesitate or entirely neglect to do so. For this study, I interviewed two high school and two elementary school teachers in institutions neighboring a Native reserve (N=4). Initial research focused on the ways in which teachers use culturally relevant pedagogy in order to engage Aboriginal students. However, primary data analysis showed that teachers were in fact not implementing a culturally relevant pedagogy in their classrooms. I sought to uncover the root of this pattern and revisited and reworked my interview questions based on this discovery. I group these findings into four key categories: 1) the teachers’ lack of knowledge, 2) the “White Voice” and its implications on a multicultural audience, 3) a lack of support from colleagues or administration, and 4) a fragile relationship with the community. After contextualizing and unpacking each of these, I conclude by providing accessible strategies that teachers can implement in order to support more inclusive practice with regard to Aboriginal culture and history.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.016 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".