Mediated Meat: Buying and Selling Beef in Canadian Supermarkets
Bibliographic record
Abstract
Revealing supermarkets as significant mediators of food ethics, this dissertation delves into the contemporary “beef” with beef. Critics wonder if ethical beef consumption is possible, since dominant beef systems have been associated with environmental deterioration, health crises, and unsafe work. As supermarkets are primary places where people encounter beef, they are valuable sites for research on these deliberations.\n\nThe ubiquitous role that supermarkets play in beef distribution is indicative of the power they hold at key junctures of the material and affective networks that facilitate foods’ movements. To assess the extent to which supermarkets promote ethical beef consumption through these “infrastructures of feeling”, the dissertation presents a case study of beef shopping in supermarkets owned by Canada’s largest food retail company, Loblaw Companies Limited. The study compiles evidence from promotional materials, in-store observations, expert interviews with management, and focus groups with shoppers. Using critical discourse analysis, it investigates the relationships between beef shopping participants that are expressed in the data.\n\nThe project builds on the literature of “cryopolitics” (the governance of frozen time-spaces between life and death) to characterize Loblaw’s supermarkets as “cryopolitical mediators” that shape conditions for flourishing in Canada’s cattle-beef infrastructure. Chapters address central themes that emerge from the data—trust, choice, ghosts—to depict multiple interpretations of the (un)ethical beef futures that Loblaw offers: from support for Loblaw as a credible risk manager; to critiques of Loblaw’s activities that have been reinvented as corporate social responsibility initiatives; and rejections of Loblaw’s plans for beef provisioning, as inspired by haunting signs of organic mutability. The study finds that Loblaw approaches ethical beef consumption through a logic of freshness, whereby profitable elements of the current cattle-beef infrastructure are preserved based on perpetually deferred promises of nourishment.\n\nThe dissertation reimagines the apparent gap between eaters and food sources that has been blamed for perpetuating the harms of the beef industry. Whereas an emphasis on separation invokes corrective efforts to fill in missing information, the framework of mediation shifts attention to the work of adjusting perceptions in the interest of finding responses to the relational entanglements of eating and being meat.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.022 | 0.012 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".