How do pesticide retailers recommend pesticides to farmers in rural China?
Bibliographic record
Abstract
Using survey data from 242 pesticide retailers, this study attempts to uncover how pesticide retailers in China make recommendations to farmers and identify influencing factors on those recommendations. Our data include a total of 586 recommendations to farmers about pesticide use from the 242 retailers. The study finds that, among approximate one quarter of the recommendations, the recommended types of pesticides cannot control the insects and diseases that affect farmers’ crops. Retailers typically recommend pesticide overuse more than appropriate use or underuse of pesticides. The Probit estimation results illustrates that government inspection, years in doing business, and information from government agricultural extension institutions are positively associated with the likelihood of retailers recommending the correct use of pesticide, while participation in technology training organized by pesticide firms reduces the likelihood of retailers recommending the correct use of pesticides. Furthermore, there is a positive association between having relatives who are pesticide retailers and the likelihood of retailers making recommendations of pesticide underuse, while there are negative associations between both being registered with the authorities and years in doing business with the likelihood of retailers making recommendations of pesticide overuse. Retailers in township seats and villages tend to recommend wrong pesticide types and excessive amounts of pesticides to farmers. Policy implications of the findings are then discussed.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".