Analysis of Government Support, Network Adaptability and Online Sales Adoption Behavior of Fruit Farmers: Based on the Survey Data of Gannong
Bibliographic record
Abstract
Online sales are an important part of agricultural product sales channels under the background of rural revitalization in the new era. Online sales play an active role in invigorating the agricultural product market, solving the problem of connecting small production with big market and promoting farmers' income, agricultural efficiency and rural welfare. Based on the field survey data of 511 fruit farmers in Jiangxi Province, this paper empirically analyzes the influence of government support and network adaptability on online sales behavior of fruit farmers by using binary Logistic model. The results show that the government's implementation of operating subsidies, preferential taxes and fees and technical training are helpful for fruit farmers to adopt online sales; Network adaptability can significantly positively affect online sales of fruit farmers, and at the same time, there is a positive adjustment between government support and online sales behavior of fruit farmers; By implementing various support measures, the government helps to improve the adaptability of fruit farmers' network, and further has a significant positive impact on fruit farmers' adoption of online sales behavior. The results provide theoretical basis for the relationship among government support, network adaptability and online sales of fruit farmers, and provide practical enlightenment for the government and fruit farmers.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".