Digesting Data: Tracing the Chromosomal Imprint of Scientific Evidence Through the Development and Use of Canadian Dietary Guidelines
Bibliographic record
Abstract
The Eating Well With Canada’s Food Guide (CFG), which represents Canada’s official dietary guidelines, is designed to address high rates of obesity and diet-related chronic disease in Canada. This article presents a qualitative study of the social and ideological actions that the CFG performs. The study draws on the concepts of antecedent genres and uptake from rhetorical genre studies, applying them in a multimodal analysis of the CFG and interviews with the CFG’s producers and registered dietitians (RDs) who work with vulnerable populations. Findings reveal that scientific representations play a profound role in the social and ideological actions that the CFG performs. The author demonstrates how representations of scientific evidence from nutrition science, as exemplified in the concept of the Food Guide Serving, are taken up by the CFG and, in turn, how these scientific representations influence RDs’ use of the CFG and dominate, rather than facilitate, discussions about healthy eating. The study suggests that the CFG, instead of being an enabling resource, is a limiting document: It limits who can make healthier food choices and how such choices can be made.
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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 | 0.102 | 0.212 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.019 |
| Science and technology studies | 0.037 | 0.055 |
| Scholarly communication | 0.023 | 0.010 |
| Open science | 0.005 | 0.018 |
| Research integrity | 0.004 | 0.007 |
| 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".