Research on the Impact of Digital Economy on Farmers' Wealth and Prosperity- From the Perspective of Enhancing Rural Human Capital
Bibliographic record
Abstract
The digital economy, as an important driving force for promoting high-quality economic development, has a significant impact on the prosperity and well-being of farmers. Based on the panel data of Chinese provinces from 2013 to 2020, this article first elaborates on the theoretical mechanisms between the digital economy, rural human capital, and farmers' wealth and prosperity. Based on this, empirical research is conducted around the relationship between the three. Research has found that: firstly, the estimated coefficient of the digital economy on farmers' wealth and prosperity is significantly positive, indicating that the development of the digital economy has significantly improved the level of farmers' wealth and prosperity; Secondly, rural human capital plays a certain intermediary role between the digital economy and the prosperity of farmers. The development of the digital economy enhances the level of rural human capital to improve the level of farmers' prosperity; Thirdly, the positive effect of the digital economy on the prosperity of farmers will be influenced by the single threshold of rural human capital, and compared to low levels of rural human capital, this positive effect is significantly enhanced at high levels of rural human capital. Therefore, in order to effectively promote the improvement of farmers' wealth and prosperity, a differentiated and dynamic digital economy development strategy should be implemented.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".