How to Improve Farmers’ Green Production Level in a Targeted Manner?
Bibliographic record
Abstract
Improving farmers’ green production behavior can guarantee food safety at the source. In recent years, a rising number of studies have focused on food safety management and have provided general regulation recommendations. Unlike many studies, this study aimed to find targeted policy recommendations according to different spatial aggregations of non-green production behavior. In the current study, more than 800 tea farmers located in the Qinba and Huangshan Mountain regions of China were investigated. An order logit model was employed to evaluate the impact of government regulation and community governance on ignorant or unkind non-green tea production behavior. Furthermore, a multi-valued treatment effect model was also recruited to demonstrate the average treatment effect of government regulation and community governance. The results show that the difference in farmers’ ignorant or unkind non-green production behavior between regions is substantial but is similar in the same region. Farmers’ non-green production behavior is negatively impacted by community governance but not significantly affected by government regulation. Government regulation can effectively inhibit the occurrence of farmers’ unkind behavior, while community governance can effectively cause farmers’ unkind behavior. Thus, some measures to reduce local farmers’ non-green production behavior should be put forward according to the differences in the spatial distribution of non-green production behavior and the differences in the effect of government regulation and community governance.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".