The Character and Economic Preference of City Network of China: A Study Based on the Chinese Global Fortune 500 Enterprises
Bibliographic record
Abstract
Based on the data of Chinese enterprises that entered the Fortune 500 list in 2015, this paper utilizes the eclectic model to construct the inter-city association network. Using the network analysis method, the spatial connection characteristics of 311 inter-city networks at prefecture level and above and 20 urban agglomerations networks in China are examined, respectively. The research found the following: (1) the overall connectivity of city network is poor, the centripetal concentration is strong, and the network is not complete. The city network structure shows three tendencies, with a concentration in political centers, a concentration in coastal areas, and a concentration in resource-based cities. The external economic dependence of each node city in national city network is high, and the city network structure has distinctly flattening characteristics. (2) Network function of cities is obviously different in multiscale region. Large cities and regional centers have more balanced function systems than the small- and medium-sized cities do. (3) The network of urban agglomerations is characterized by decentralization of power, differentiation of status, and dependence on external connections. The radiation effect of three major urban agglomerations in coastal China is strong, but the radiation effect of other urban agglomerations needs to be strengthened. (4) Both city networks and agglomeration economies have positive impact on economic growth of the city. The economic performance of city networks is differentiated between urban agglomeration cities and nonurban agglomeration cities, as well as between cities of different scale levels. This study provides new evidence for understanding the spatial relations and expansion of Chinese city networks.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".