Get Over Yourselves, We are Doing This as a Team:" Teaching Canadian History Beyond the Eurocentric Lens
Bibliographic record
Abstract
The Ontario History curriculum has undergone significant changes since the 1970s to better represent the voices, experiences, and perspectives of ethnic minority groups living in Canada. These changes indicate that more secondary school educators are committed to teaching Canadian History beyond the Eurocentric lens. By using a qualitative research approach involving a literature review and semi-structured interviews, this study explored the ways in which two History educators implement diverse content and perspectives in grade 10 Canadian History. Both participants believe that implementing diverse content in Canadian History improves the quality of their lesson plans, helps students understand the complexity of Canada’s national narrative, and better prepares students to handle real-life conflict as they develop strategies for dealing with controversial issues. These findings suggest that secondary school History teachers who are dedicated to infusing diverse content are highly sensitive to the needs of others and aware of how their own privilege and social position may contrast that of their students. They are determined to fostering a generation of active Canadian citizens who will be strong advocates for social justice issues.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.024 | 0.008 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.028 | 0.003 |
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".