Measuring the implementation effects of rural revitalization in China’s Jiangsu Province: Under the analytical framework of “deconstruction, assessment and brainstorming”
Bibliographic record
Abstract
Abstract With rural decline becoming a global issue, the implementation of rural revitalization strategy has been turning into general starting points for many countries, especially in contemporary China. In this paper, we proposed a systematic analysis framework and a new evaluation index system for the implementation effectiveness of rural revitalization at the county scale. In addition, empirical research was conducted based on Jiangsu Province. The results indicated that the average value of the comprehensive index of implementation effects of rural revitalization in Jiangsu was 1.8198, and nearly 60% counties in the province were below this average. Also, the obvious disequilibrium and spatial differentiation laws were observed which presenting a stepwise advance from south to north, with the inland superior to the coastal. The five‐dimensional coordination status of rural revitalization in Jiangsu was relatively high, with the majority of counties reached at barely balanced state. A total of 21 counties were identified as problematic regions in Jiangsu through comprehensive overlay analysis. They were mainly located in northern Jiangsu, and could be divided into seven specific types. The proposed evaluation framework and indicator system might make up for the lack of effective reference systems and scientific quantitative indicators in the current county‐scale rural revitalization strategy practices. Moreover, the idea of identifying problem areas and the classification mode of rural revitalization goal levels proposed in this paper are also of great reference for other regions.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".