A Pedagogy of Reconciliation: Transformative education in a Canadian context
Bibliographic record
Abstract
Of the 94 Calls to Action within the Truth and Reconciliation Commission of Canada’s (TRC) Final Report, almost one-fifth focused on matters of education. This represents a strong belief that formal teaching and learning can positively impact the relationship between Indigenous and non-Indigenous people in Canada. However, there is no established framework for such education. Reflecting on the report and drawing on critical pedagogy scholarship, I work towards a better understanding of the necessary pedagogy required for education for reconciliation. Recognizing the ways in which the work of “reconciliation” is situated in particular cultural, historical, and social realities, I outline an approach to education for reconciliation that is attentive to the Canadian context. Drawing on both critical pedagogy and Indigenous knowledges, this framework attempts to honour the TRC Final Report, offering an approach that is both pointedly critical and deeply relational.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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 teacher head, 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".