Racism in Canadian Elementary School History and Social Studies Textbooks
Bibliographic record
Abstract
Do Manitoba elementary schools? history and social studies textbooks contain racist knowledge towards Indigenous peoples in Canada? Data is collected from a range of textbooks that are published between 1960 and 2013; all were found in schools? libraries and classrooms within the past year. Youth are using even the dated books for research, and therefore consider them legitimate academic sources. The more recent publications are listed on the Manitoba Textbook Bureau, a government agency that designates acceptable books for teachers to use in the province. Surveying these textbooks illuminates various problematic ways that race and Indigenous peoples are taught and portrayed. Older textbooks rely on overtly racist rhetoric, such as labelling Indigenous peoples ?barbarians, ? ?Noble Savages, ? or suggesting that white settlers were the first people to live in Canada. More recent textbooks move away from this open racism towards a new subtle racism that blurs the lines between learned cultural traits and biological characteristics, essentializing social features. The notion that skin colour provides any deep genetic meaning has long been scientifically disproven. A result of this new, covert racism found in schools? textbooks, combined with the accessibility of old overtly racist ones, is that racialized thinking becomes normalized amongst youth early on.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.012 | 0.025 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".