The Confused Canadian Eater: Quantification, Personal Responsibility, and Canada’s Food Guide
Bibliographic record
Abstract
Canada’s Food Guide is promoted as an educational tool that translates nutrition for laypeople and provides tools to measure eating and its effects on the body. However, the discourses it circulates have been critiqued as abstract and difficult to apply in everyday practice, and linked to a nutritionally confused environment where the disempowered eater is positioned as lacking knowledge about nutrition and in need of expert intervention to learn how to eat right and become a responsible, healthy subject. By mobilizing a biopolitical frame, this article takes a closer look at the work Canada’s Food Guide does in constructing particular ideas about nutrition, and at the issues of confusion and personal responsibilization that emerge through its quantitative healthy eating discourse. This work turns to literature on scientific and quantitative languages that drive nutrition guidance in texts like Canada’s Food Guide, namely, the concepts of “discourses of quantification” and “nutritionism,” which prioritize scientific knowledge about food while excluding complex economic, political, and sociocultural issues tied to how we eat. In light of Health Canada’s ongoing revision of the food guide, this work seeks to add to discussions about how ideas of healthy eating may be renegotiated with the goal of enriching the way future Canadian public health initiatives and nutrition policies are constructed.
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.050 | 0.066 |
| Scholarly communication | 0.015 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".