Teaching the history of scientific racism: A critical imperative for anti-racist pedagogy
Bibliographic record
Abstract
Drawing from a review of literature that has explored the history of scientific racism, this article considers how understanding the history of race, as an 18th- and 19th-century invention of Western Europe and the United States, can enhance Canadian anti-racist teacher education. I begin with a review of key conceptual building blocks of race — racial categories, racial hierarchies, White male intellectual superiority, and racial purity — and then outline pivotal historical stages that led acclaimed researchers to denounce race science in the mid-20th century. To conclude, I draw from anti-racist theory to discuss implications for present-day Canadian teacher education regarding who benefits from racism, who can be racist, school-based deficit and essentialist racist practices, and K–12 curricular connections.
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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 | 0.027 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.025 | 0.119 |
| Scholarly communication | 0.016 | 0.010 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.016 |
| Insufficient payload (model declined to judge) | 0.003 | 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".