Anti-Blackness and Orientalism in Quebec and Manitoba Ancient History Curricula
Bibliographic record
Abstract
Although Canada is often portrayed as a multicultural, benevolent, liberal society, the experiences of Black peoples, Indigenous peoples and Peoples of Colour living in Canada point to the problematic of ongoing anti-Black racism, Indigenous erasures and anti-immigrant sentiments, while perpetuating White Eurocentric dominance. Research demonstrates that schools and school curricula play an important role in perpetuating these problems (e.g., Abdou, 2017; Calderon, 2014; Poole, 2012). But how might curricula and available teaching resources specifically be contributing to Canada’s underlying narratives of White Eurocentric dominance? There is a growing body of literature that demonstrates problematic discourses in the curriculum for Indigenous peoples (e.g., Battiste, 2013; Calderon, 2014; Tuck & Gaztambide-Fernández, 2013). In this paper, we specifically interrogate anti-Blackness and Orientalism. We outline the various findings of critical discourse analyses that we conducted on secondary school textbooks used in Quebec and Manitoba to teach world history and ancient civilizations. By comparing these two contexts, we offer new perspectives on the ways that Canadian curricula are constructed as dominant White-centric narratives by depending on the logics of Orientalism and anti-Blackness. Building on previous textbook analyses, we attempt to bring critical perspectives to problematize dominant norms that contribute to the oppressions of Black peoples and Peoples of Colour in the Canadian context and to provide insights on a potential way forward for more inclusive and balanced representations. While our textual analyses do not directly address representations of Indigenous peoples in curricula, we hope this contribution will help draw attention to some common exclusionary approaches and representations that need to be questioned and challenged.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.028 | 0.016 |
| Scholarly communication | 0.007 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".