Assessment system and empirical study on China’s local governance quality
Bibliographic record
Abstract
Good governance is an important guarantee for the sustainable development of regional economy. Since the Reform and Opening-up, China's local economic development has made remarkable achievements, which has aroused the concern of the academic community. The main contents of China's local governance quality assessment include: government capacity, marketization, and rule of law, which means a new multidimensional perspective on local governance assessment: "power allocation + governance capacity + rule of law". This paper designs the index system of China's local governance assessment based on literature investigation and coefficient of variation, which utilizes two approaches: qualitative analysis and quantitative analysis. Furthermore, the investigation analyzes the panel data (2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014) of provincial regions in China by utilizing global factor analysis and weighted average. The results indicate that the improvements in government capacity, marketization, and rule of law were higher in the majority of provinces studied in central and western China between 2001 and 2014, while they were lower in most of provinces researched in northeastern China in the same period, which resulted from policy differences. In 2014, government capacity, marketization, and rule of law were higher in the majority of provinces investigated in eastern China, while they were lower in most of provinces surveyed in the rest of China, which was due to the differences in policies support, resources management, absolute geographical location, per capita GDP, and market economy. In order to improve local governance quality and achieve sustainable economic growth, it is very important to establish local governance structure as "effective government + efficient market", China should pay more attention to weak aspects of local governance and some provincial regions in western, central and northeastern China.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".