Social Reinforcement of Environmentally Conscious Consumer Behavior at a Grocery Store Cooperative
Bibliographic record
Abstract
Cooperative, natural grocery stores set themselves apart in many ways from the corporate, for-profit stores that are often seen as more mainstream in the United States. Created through local grassroots efforts, such cooperatives tend to support environmental efforts like local, sustainable and organic agriculture, and to offer environmentally friendly foods that are low on the food chain and/or contain little embodied energy. A feeling of belonging can be a powerful motivator to shop at the co-op, and even to join the organization. Such in-group experiences serve both to build and maintain relationships and to differentiate the cooperative from other grocery outlets, reinforcing the social preferences toward environmental conscious consumer behavior in such retail outlets. This qualitative study explores one local cooperative grocery store through a symbolic interactionism lens, asking whether and how community is built through shoppers’ verbal interactions with co-op staff. Ethnographic methods are used to highlight and explore shoppers’ interpretation of the “co-op” experience, and how that interpretation is communicated through social interaction. Themes found in the data indicate that both customers and staff see the community cooperative as not only a place to shopbut also as a place to interact with likeminded people, about topics and issues integral to their sense of identity, especially in the area of environmentally conscious consumer behavior.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".