Who Does Not Attend Farmers Markets and the Community Supported Agriculture (CSA) Programs?
Bibliographic record
Abstract
This study uses data from 172 consumers who participated in mail survey distributed in 2020 to within the Southcentral Kentucky region. The purpose was to analyze consumer habits of attending farmers markets focusing on characteristics of those who do not attend and analyze their likelihood to participate in the Community Supported Agriculture (CSA) program. We used both Multinomial and Ordered Logit models to analyze data. Results indicate that respective relative probabilities for “never attend”, “attend occasionally”, and “attend frequently” are 55%, 29%, and 16%, respectively. Male consumers, rural residents, primary shoppers, and those with a 2-year associate degree are less likely to attend farmers markets. This study finds that educated consumers and those who were satisfied with previous market experiences are more likely to attend a market frequently. Another finding is that consumers with interests in using an App to purchase fresh produce are more likely to attend farmers markets and participate in CSA programs. We further found that consumers are less likely to join a CSA program if they live in a rural area. This study contributes to the understanding of characteristics of consumers who do not use direct-to-consumer market outlets, particular farmers’ markets, and CSA programs. It informs policy makers who seek to promote these two market outlets. This study is also useful to managers of farmers markets and CSA programs when making marketing decisions.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".