The Spillover Effects of High‐Speed Railway Networks From the Perspective of Industrial Agglomeration
Bibliographic record
Abstract
As an important part of modern transportation infrastructure, high‐speed rail (HSR) networks not only reduce the spatiotemporal distance between regions but also generate widespread spillover effects through mechanisms such as population mobility, technological innovation, and market expansion. Based on the city‐level panel data from 2008 to 2021, this paper uses a spatial econometric model and a generalized structural equation model (GSEM) to study the spatial spillover effects of HSR networks on the three industrial agglomerations and tests the impact mechanism of HSR networks on industrial agglomeration. We find that HSR networks significantly inhibit the agglomeration of primary and secondary industries while significantly promoting that of the tertiary industry. Regional heterogeneity analysis shows that HSR networks have a negative impact on the secondary industry agglomeration in the eastern region but obviously promote the tertiary industry agglomeration, and their promotion effect on the tertiary industry is also significant in the central and western regions. The results of the mechanism test show that HSR networks significantly affect the agglomeration of the three industries through the path of population mobility, technological innovation, and market scale.
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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 | 0.000 | 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".