New online market connecting Chinese consumers and small farms to improve food safety and environment
Bibliographic record
Abstract
Abstract In emerging economies where small farms are the main source of food supply, it is costly for the government to monitor and control food safety and production impact on the environment. However, the online food market can potentially give farmers stronger incentives to supply safer and more eco‐friendly products, as they can access a national market where consumers are interested in healthy food, and they can differentiate their products by providing production information using videos and pictures. This research uses the choice experiment method to elicit farmers’ preference for production practices and marketing channels in China where e‐commerce and delivery businesses are fast‐growing. Our main finding is that farmers perceive higher utility in selling safer and more eco‐friendly products than conventional products when using e‐commerce platforms, evidence of the online market's positive role in food safety enhancement. Our results also identify two types of farmers: traditional farmers and farmers open to the idea of online markets. Farmers who have higher education and live in villages with e‐commerce service centers are more likely to be the latter.
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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 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".