Application of Numerical Methods in the Study of the Impact of Agricultural Subsidy Policies on the Development of China’s Soybean Industry
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Bibliographic record
Abstract
Due to its heavy reliance on imports, the futures and spot markets of China's upstream and downstream soybean products are vulnerable to the impact of the international market.In order to guarantee the security of the soybean industry, China introduced corresponding agricultural subsidy policies in 2008, 2014 and 2018, respectively.In order to test the impact of the subsidy policy on the development of the soybean industry, this paper utilizes an empirical mathematical planning model to evaluate the implementation effect of the subsidy policy for soybean producers ex ante, and explores the defects of the agricultural subsidy policy by simulating the production decisions of farmers.It also measured the efficiency of soybean subsidy, the efficiency of agricultural machinery purchase subsidy and the efficiency of agricultural insurance premium subsidy using a three-stage DEA model.In the empirical research part, the constructed numerical method of soybean producer subsidy policy unfolds the effect assessment.The empirical results show that the implementation of the soybean producer subsidy policy increases the proportion of soybean planting and soybean total factor productivity by 9.47% and 17.43%, respectively, and that the soybean producer subsidy policy has a facilitating effect on the expansion of soybean planting and total factor productivity.Accordingly, five policy recommendations are put forward with a view to promoting the healthy development of the soybean industry.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 it