Government Competition, Transportation Infrastructure Construction and Industrial Agglomeration
Bibliographic record
Abstract
This paper uses the prefecture-level data from 2003 to 2016 of the Yangtze River Delta region of China and uses the spatial Dubin model(SDM) to study the promotion effect of transportation infrastructure on industrial agglomeration and the spatial spillover effect: First, it is found that there is a significant positive spatial correlation between transportation infrastructure development at the prefecture and city level governments in China, which supports the hypothesis of intergovernmental transport infrastructure competition in this paper; secondly, the regression of the spatial measurement model proves that transportation infrastructure has a certain role in promoting regional industrial agglomeration, and enhances the level of industrial agglomeration in the surrounding areas through spatial spillover effects; in addition, this article uses the Spatial Dubin Model(SDM)) partial differential decomposition method to explore the spatial spillover effects of transportation infrastructure on the flow of elements (this paper explores the spatial spillover effects of transport infrastructure on factor mobility using a partial differential decomposition of the Spatial Durbin Model(SDM)); finally, the robustness of the results of this paper is tested using the replacement space weight matrix and estimation method.
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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 | 0.001 |
| 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".