UNLEARNING COLONIAL IDENTITIES WHILE ENGAGING IN RELATIONALITY: SETTLER TEACHERS’ EDUCATION-AS-RECONCILIATION
Bibliographic record
Abstract
Before the TRC’s Calls to Action, we were a collaborative teacher-education partnership of Anishinaabekwe and White settler researching and teaching reconciliation as pedagogical practice with five cohorts of settler teacher-candidates. Engaging theories of settler-colonialism, decolonization and Indigenous studies, we outline the obstacles and struggles in settler teacher education, such as exposing the legacies of colonialism in education, cultural harms and systemic racism in curriculum, and ongoing ignorance as entitlement by teachers. In addition, we focus on the complexities of methods for improving respectful relationality with Indigenous students and community as well as our hopes in helping new teachers commit their professional practice to focus on supporting Indigenous children and youth.
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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 | 0.012 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.023 | 0.018 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.002 | 0.006 |
| 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".