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 .
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".