How Canadians Communicate VI: Food Promotion, Consumption, and Controversy
Bibliographic record
Abstract
Food nourishes the body, but our relationship with food extends far beyond our need for survival. Food choices not only express our personal tastes but also communicate a range of beliefs, values, affiliations and aspirations—sometimes to the exclusion of others. In the media sphere, the enormous amount of food-related advice provided by government agencies, advocacy groups, diet books, and so on compete with efforts on the part of the food industry to sell their product and to respond to a consumer-driven desire for convenience. As a result, the topic of food has grown fraught, engendering sometimes acrimonious debates about what we should eat, and why. By examining topics such as the values embedded in food marketing, the locavore movement, food tourism, dinner parties, food bank donations, the moral panic surrounding obesity, food crises, and fears about food safety, the contributors to this volume paint a rich, and sometimes unsettling portrait of how food is represented, regulated, and consumed in Canada. With chapters from leading scholars such as Ken Albala, Harvey Levenstein, Stephen Kline and Valerie Tarasuk, the volume also includes contributions from “food insiders”—bestselling cookbook author and food editor Elizabeth Baird and veteran restaurant reviewer John Gilchrist. The result is a timely and thought-provoking look at food as a system of communication through which Canadians articulate cultural identity, personal values, and social distinction.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.050 | 0.026 |
| Scholarly communication | 0.019 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".