Research on regional economic high-quality development and fiscal policy regulation path under big data computing analysis
Bibliographic record
Abstract
Under the background of economic transformation and high-quality development strategy, the coordinated development of regional economy and precise regulation of fiscal policy have become a hot spot of concern.This paper constructs the evaluation index system of regional economic highquality development and evaluates the level of economic high-quality development in the recent 10 years by using the entropy weight-TOPSIS method.Combined with Dagum Gini coefficient and spatial autocorrelation test, we study the spatial correlation of economic high-quality development among regions.The benchmark regression model and mediation effect model are constructed to calculate the effect of fiscal policy on the level of regional economic high-quality development, and to judge the effectiveness of fiscal policy regulation path.The study shows that the 30 provinces in China can be divided into different regions according to the level of high-quality economic development, and each region presents different types of characteristics such as "high -high", with significant differences.The variables in the benchmark regression model and the mediation effect model are correlated at the 1% level, and pass the smoothness test when the difference is of the 0th order.Fiscal policy at the regional level has a positive contribution to the level of high-quality economic development, but at the same time is affected by the original level of development of each region.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".