Eating Clean: Anti-Chinese Sugar Advertising and the Making of White Racial Purity in the Canadian Pacific
Bibliographic record
Abstract
Between 1891 and 1914, western Canada’s largest sugar manufacturer – BC Sugar – constructed a racialized discourse of food cleanliness. This discourse argued that Chinese-made sugars were contaminated while Canadian-made sugars were clean. Through an analysis of this discourse, this article argues that BC Sugar constructed a purity/polluted binary that suggested that white consumers’ racial purity was threatened by Chinese-made sugars. It then links BC Sugar’s clean foods campaign to three broader trends. First, it illustrates that BC Sugar’s construction of pure versus polluted foods supported the effort to establish white supremacy in the Canadian Pacific. Second, it demonstrates that discourses of food purity enabled white settlers to construct bodily purity by the eating of so-called clean foods. Third, it argues that since contemporary discourses of food cleanliness rely on pure versus polluted metaphors, scholars must attend to the motivations driving today’s clean eating movement.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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; a candidate call from one teacher head, 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".