Finding balance in teaching Indigenous Studies and settler colonialism
Bibliographic record
Abstract
eaching Indigenous histories has always been a journey for me.I am a non-Indigenous White settler-scholar who teaches Indigenous histories to primarily non-Indigenous students in a large, urban, multicultural university.I occupy a place of discomfort.I was drawn to this place of discomfort because I grew up in a small prairie town, where Indigenous and settler inhabitants grew up together, went to school and church together, worked together, and lived beside one another.Racism, cooperation, and compassion existed side by side.I wanted to understand the deep history of my town.My personal story on the land began at the turn of the 20th century when the Canadian government sponsored my Ukrainian great-grandparents to come to Manitoba to farm the land.I wanted to go deeper, to find out who occupied the land since time immemorial, and I was drawn to the histories of Métis, Anishinaabe (Ojibwe), and Nehinaw (Cree).Over time, I got my PhD and found a job in the History Department at York University.I carved my academic life as an ally, researching the histories of colonial encounters in the fur trade and building courses about early Canada, which were necessarily dominated by Indigenous stories.I want to share some illustrative stories of my journey in finding ways to best teach Indigenous histories.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.025 | 0.037 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 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".