Identifying and working through settler ignorance
Bibliographic record
Abstract
As Canadian education systems implement the Truth and Reconciliation Commission’s Calls to Action, various expressions of white settler resistance become amplified. This article examines the potential for settler-educators’ stories to teach about processes for working through settler ignorance. Insight into the question of how to transform settler subjectivities and relationships with Indigenous peoples cuts across theoretical terrain in three fields: decolonizing education, epistemic ignorance, and affect/felt theory. We engage with these currents to analyze settler resistance through nIshnabek de’bwe wIn, a project aimed at transforming relationships between Indigenous and non-Indigenous students and teachers through collaborative storytelling. We report on one project facet that brought Indigenous and non-Indigenous researchers, educators, and students together to create digital/multimedia stories about experiences of schooling that could inform settler-educator learning by offering critical insight into unlearning ignorance as one strategy (among many) for decolonizing colonial structures of schools. Attention to settler stories reveals a triadic relationship between power/knowledge/affect wherein these forces are inextricably entangled in ways that create and reinforce the epistemological knot of settler ignorance and resistance. The emotional work storytellers undertook as part of their embodied learning offers insight into the promise of creative pedagogies for untying that knot.
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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 | 0.017 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.042 | 0.046 |
| Scholarly communication | 0.016 | 0.009 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".