Do Online Shoppers Attend Farmers’ Markets?
Bibliographic record
Abstract
Online shopping is changing ways in which offline markets operate. As the online shopping for fresh produce takes off, it is important to investigate its effects on existing physical market outlets. The main objective for this study is to explain how often online shoppers attend farmers’ markets. The study uses data that was collected in 2016 from a sample of 1,205 consumers residing in the south region of the United States who made at least two online purchases within six months prior to participating in this study. This study employed a multinomial Logit model and Stata was used to run the regression. Results show that the majority of these online shoppers never attended a farmers’ market. The relative probabilities for the online shoppers to “never” attend farmers’ markets, attend “occasionally”, and “frequently” are 0.54, 0.28, and 0.18 respectively. We found that the lack of awareness, inconvenient place and/or time, and low interests are major reasons for nonattendance. This study suggests that farmers’ markets could greatly benefit by developing marketing strategies targeting online shoppers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| 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 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".