Food activists, consumer strategies, and the democratic imagination: Insights from eat-local movements
Bibliographic record
Abstract
Scholars remain divided on the possibilities (and limitations) of conceptualizing social change through a consumer-focused, “shopping for change,” lens. Drawing from framing theory and the concept of the democratic imagination, we use a case study of “eat-local” food activism to contribute to this debate. We ask two questions: first, how do activists in the local food movement come to diagnose and critique the conventional industrial food system? and second, what roles do they envision for participants in the sustainable food movement? We address these questions by drawing from activist interview data (n = 57) and participant observation of the eat-local movement in three Canadian cities. Our findings illuminate a mixed picture of possibilities and limitations for consumer-based projects to foster social change. On the one hand, the diagnostic frames presented by food activists suggest skills in critical thinking, attention to structural injustice, and widespread recognition of the importance of collective mobilization. This framing suggests a politically thick democratic imagination among eat-local activists. In contrast, when it comes to thinking about prescriptions for change, activist understandings draw from individualistic and market-oriented conceptualizations of civic engagement, which indicates a relatively thin democratic imagination. These findings demonstrate that despite the sophisticated understandings and civic commitment of movement activists, the eat-local movement is limited by a reliance on individual consumption as the dominant pathway for achieving eco-social 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.024 | 0.051 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| 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".