What We Talk About When We Talk About Canadian History: The Whiteness of Canadian History and Social Studies Education
Bibliographic record
Abstract
In this paper , I will focus on White ness in Canadian history and social studies education. Whiteness can operate as a concept, identity, as power, as privilege . It is, as Himani Bannerji (2000) writes , the “valorized expression of European racist-patriarchy” (107). Using the experiences of one Canadian history teacher , I will show how a teacher can maintain White supremacy in teaching and learning Canadian history despite their articulated commitments to antiracism and inclusion. Th e s e experiences then invite larger questions of how White ness can get coded in K to 12 History and Social Studies teaching as the “national” and “dominant” story with an ( un) intentional add-and-stir quality of the experiences of people of colour. In this paper, I highlight the ways that emphasizing multiculturalism or teaching through different perspectives can work to ratify White ness as a through line in teaching and learning Canadian history , by maintaining Whiteness as a centre of national legitimacy .
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.067 | 0.056 |
| Scholarly communication | 0.019 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 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".