Decolonizing History Curricula Across Canada: Recommendations for (Re)design
Bibliographic record
Abstract
This study explores how K–12 history curricula across Canada currently address—and may better address in future—decolonizing imperatives. Following a consideration of the limitations and strengths of curricula in this regard, the article identifies five recommendations for (re)designing history and social studies curricula with decolonizing goals in mind: (1) challenge hegemonic narratives, (2) value Indigenous ways of knowing and being, (3) reflect on privilege and positionality, (4) engage in the ethical dimension, and (5) focus on the future. Each recommendation is informed by the empirical study of curricula, and positioned in relation to scholarly conversations about the responsibility of history education that seeks to respond to calls for decolonization.
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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 | 0.270 | 0.302 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.013 | 0.025 |
| Science and technology studies | 0.030 | 0.015 |
| Scholarly communication | 0.032 | 0.015 |
| Open science | 0.012 | 0.018 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 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".