Are Online Shoppers Interested in Learning about Locally Grown Fresh Produce?
Bibliographic record
Abstract
This study describes online shoppers, explains their interests in learning about market outlets for locally/regionally grown fresh produce, and analyzes their preferences for channels to receive educational information concerning local/regional fresh produce. We used a K-mean clustering algorithm together with binary and ordered Logit models to analyze data collected in 2016 from a stratified randomly selected sample of 1,205 online shoppers within the U.S. South region. We found that the probability for online shoppers to be interested in learning about market outlets for local/regional grown fresh produce is 66 percent. Results also indicate that the likelihood for the word-of-mouth to be at least preferred (preferred, very preferred, and extremely preferred) as channel to receive educational information about local fresh produce is 69 percent. The probabilities for local radio/TV stations, Internet-based, newspapers, and ads on public places to be at least preferred are 61 percent, 48 percent, 57 percent, and 66 percent respectively. Findings from this study are useful for fresh produce growers, agricultural marketers and educators, online shoppers, and further research studies.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".