Fiscal Transparency and Public Service Quality Association: Evidence from 12 Coastal Provinces and Cities of China
Bibliographic record
Abstract
This paper presents an evaluation index system of public service quality, which contains 35 indexes from the dimensions of the output and effect. Based on data from 2010 to 2017 in 12 coastal provinces and cities of China, this paper assesses public service quality by using the methods of entropy weight order preference similarity to the ideal solution (TOPSIS) and analyzes the effect of fiscal transparency on public service quality. The results show that the public service quality in the 12 coastal provinces and cities of China studied is relatively high, and fiscal transparency has a positive effect on public service quality. This analysis showed that an increase of 1% in fiscal transparency would lead to an increase of 0.0323% in the quality of public services. Fiscal transparency contributes to the quality of public services by improving the scale of investment and the efficiency of public services expenditure; this is because fiscal transparency can increase the expenditure on public welfare services and curb official corruption. Furthermore, the proposed evaluation index can enable government administrators to take the necessary steps on the appropriate dimensions to improve public service quality. This study can provide some guidelines for other countries, especially to improve public service quality by increasing fiscal transparency.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".