How Discomfort Reproduces Settler Structures: Moving Beyond Fear and Becoming Imperfect Accomplices
Bibliographic record
Abstract
In this paper we explain how teachers can subvert settler colonial epistemology in their classrooms and become ‘imperfect accomplices.’ Drawing on a larger project, we focus on the ways non-Indigenous teachers understood their role in teaching Indigenous content and epistemologies through their lenses of ‘fear,’ which we re-theorize as ‘anxiety.’ These anxieties were enacted by the educators in two ways: stopping the teaching of Indigenous content and epistemologies, or using productive pausing for self-reflection. We explain how stopping the teaching outside of settler colonial epistemology is based on structures that impose fear to go outside of that epistemology. We then examine how some teachers pause within these structures of ‘fear’ and explain three strategies to become ‘imperfect accomplices.’
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.009 | 0.043 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".