Unpacking Our White Privilege: Reflecting on Our Teaching Practice
Bibliographic record
Abstract
MacIntyre (1981) asks, “Of what stories do I find myself a part?” (p. 201). As teachers working in an Indigenous context, we found ourselves telling stories that had moments of tension between our Eurocentric ways of knowing and the Indigenous context in which we taught. This intersection has prompted our research. We ask two questions in this inquiry: What can our experiences as non-Indigenous teachers in an Indigenous community offer us in our understanding as new researchers in the field of Indigenous education, and how can our teaching narratives further preservice teachers’ understandings of teaching Indigenous students? Through critical White studies, our research examines White privilege, power, and position and begins to unearth the experiences of teaching as non-Indigenous educators in a remote Indigenous community in Ontario, Canada. Narrative inquiry and autoethnographic methods connect our stories to greater social, political, and cultural discourses. These stories serve to disrupt the dominant discourse that divides and others the complexities of Indigenous education. This work will interrogate and unpack our White privilege and power and will serve to assist preservice teachers in their understanding of teaching within Indigenous contexts.Keywords: Indigenous education; narrative inquiry; critical White studies; teacher education
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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 | 0.013 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.043 | 0.071 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.006 |
| 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".