The True North Strong and Free? Casting Shadows on Whose History Students Learn in Canadian Universities
Bibliographic record
Abstract
Race-based discrimination in Canada exists at the institutional and structural level. While acknowledging its existence is a crucial first step in eradicating this particular form of discrimination, an essential second step includes implementing structural changes at the institutional level in Canadian universities. In an effort to disrupt the Eurocentricity of knowledge production this commentary argues that the Canadian government’s official historical narrative that depicts Canada as being born of the pioneering spirit of British and French white settlers fails to capture the actual history of the country. Rather, it fosters the continuation of the supremacy of whiteness thereby causing significant harm through the perpetuation of racial bias. We argue that the history and contributions of Indigenous, Black, and Chinese Canadians, all of whom were in this country prior to confederation, should be told in a mandatory university course. Our findings indicate that while a number of universities have individual courses, usually electives and some graduate degrees on Indigenous, Black, and Chinese history, there is little offered from the Canadian context and certainly nothing that is a mandatory course requirement. In addition, we suggest compulsory university staff-wide anti-racism training; the ongoing hiring of professors and sessional instructors who are racially representative of the population of Canada; and community outreach, mentorship, and counselling programs that are designed to help students who are underrepresented in Canadian universities. In our opinion, we believe that these changes have the potential to provide a lens to disrupt settler colonial spaces, mobilize race in academic curricula, and encourage social justice actions that can offer a more inclusive learning environment.
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 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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.063 | 0.033 |
| Scholarly communication | 0.019 | 0.007 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.006 |
| 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".