Digital rural construction, resource mismatch, and rural land use efficiency
Bibliographic record
Abstract
Introduction With the acceleration of the allocation of agricultural resource elements in agricultural development, the relationship between digital rural construction and rural land use efficiency has become increasingly close. Methods In order to explore the impact and underlying mechanism of digital rural construction on rural land use efficiency, this paper constructs an evaluation system index for China's digital rural construction and uses the SBM-GML model to measure rural land use efficiency. Based on this, data from 30 provinces in China from 2010 to 2022 are used to test it using fixed effects and mediation effects models. Results (1) The construction of digital rural areas can directly promote the improvement of rural land use efficiency. This conclusion still holds true after endogeneity and robustness tests. (2) Mechanism analysis shows that digital rural construction can alleviate the mismatch of land resources, capital resources, and labor resources, thereby indirectly promoting the improvement of rural land use efficiency. (3) Heterogeneity analysis shows that the construction of digital rural areas has a more significant driving effect on the efficiency of rural land use in eastern and southern regions of China, as well as in major grain producing and selling areas. Discussion This article suggests continuing to promote the development strategy of digital rural construction, improving the problem of resource mismatch, and paying attention to the regional imbalance of digital rural construction. It is necessary to maintain the leading position of “first mover advantage” areas and also pay attention to filling the gaps in “later mover advantage” areas, in order to comprehensively promote the further improvement of rural land use efficiency.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".