Does Marketization Promote High-Quality Agricultural Development in China?
Bibliographic record
Abstract
Over the past 40 years of reform and opening, the enhancement in marketization has greatly promoted the development of the Chinese economy. At present, China’s economic development model has shifted from a focus on speed to a focus on quality. Against this background, it is necessary to further promote marketization reform to promote high-quality development in China. This paper begins with an introduction to high-quality agricultural development and the degree of marketization. According to the definitions of high-quality development and marketization, we constructed an index of high-quality agricultural development and an index of marketization degree, respectively. First, we determined the characteristics of high-quality agricultural development in China. There are large regional differences in agriculture development, but these disparities are improving simultaneously, and regional differences are showing a narrowing trend, except for the western region. Then, we measured the impact of marketization reforms on high-quality agricultural development using the Quadratic Assignment Procedure. Based on sample data from 2009 to 2019, this paper found that marketization reform has played a significant role in promoting high-quality agricultural development. The three sub-indicators of non-state-owned economy, factor market, and the market’s level of order, which represent the marketization degree, had significant impacts on reducing regional differences in high-quality agricultural development. Additionally, the effects of these three variables gradually increased, narrowing the regional differences in high-quality agricultural development. Finally, we suggested that promoting the development of a non-state-owned economy, factor market, and the market’s level of order would be an important path to boosting the high-quality development of agriculture.
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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 | 0.002 | 0.001 |
| 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 | 0.000 | 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".