How do producers imagine consumers? Connecting farm and fork through a cultural repertoire of consumer sovereignty
Bibliographic record
Abstract
Abstract The phenomena of meat production and consumption are related but often studied separately, funnelled into silos of agro‐food and consumer‐focussed research. This article aims to reconnect these spheres by asking: How do meat producers understand the role of consumers in the ethical meatscape? We draw from interviews and site visits with 74 actors engaged with the ethical meat system in Canada. We find that consumers loom large in the cultural imaginary of meat producers and are often framed as key drivers of food system change. We make a two‐pronged argument that explains the complex, embedded presence of consumers in meat producers’ cultural imaginary. Conceptually, we argue that producers draw from a cultural repertoire of consumer sovereignty that frames consumer choice as a foundational element of capitalist societies. Empirically, we argue that ethical meat producers’ direct relationships with consumers infuse producers’ work with meaning and emotional significance, and this works to reinforce a normative valuation of consumer sovereignty. This research contributes to scholarship interrogating the implications of consumer‐driven models of food system change.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.042 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".