Celebrations everywhere: How social movements enact emotional culture to advance an ideology of local production
Bibliographic record
Abstract
I investigate how social movements enact emotional culture to advance an ideology of local production in the context of local food initiatives in a small city in Canada. In Localtown, a local food movement conducts initiatives to rebuild the city’s local food system after decades of agricultural consolidation had reduced the availability of local food. Starting with feasts in 2004 that drew attention to the issue, movement members began making active use of events to attract external audiences to local food initiatives. After 2007, the movement's focus went from generating awareness to building infrastructure. Farmers’ markets then became the main vehicles for mobilization. Adopting a qualitative methodology to build theory inductively, I conducted participant observation in the two main markets in Localtown, and I interviewed market vendors, consumers, and members of the broader local food movement. I found that movement members build an emotional culture that emphasizes fun and community experiences while mobilizing movement members despite a lack of agreement on how to enact shared ideals of community resilience and ecological conservation. In building this emotional culture, movement members make frequent use of cultural performances that celebrate the movement’s shared ideals and generate a fun and vibrant community spirit in farmers’ markets. A natural mise-en-scene displayed by the setting and by market products, reinforces the community and ecological meanings of the performance, attracting community audiences whose presence further reinforces perceptions of markets as a community space. But neither the meaning nor the emotionality of performances become necessarily associated with markets, as performances may ignore market products, or the emotional culture may blend into the vibrancy of other surrounding performances. My findings contribute to the literature on social movements by showing that a coherent, positive and vibrant emotional culture helps movements to mobilize external audiences despite the existence of conflicting ideologies or despite the lack of ideological affiliation. I also unveil that a positive emotional culture is often described as in opposition to the ascetic emotionality of modern institutions, revealing deeper meanings of movement-sponsored emotions. My dissertation also offers contributions to literatures on cultural performances, communities, and crafts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".