World City Network in China: A Network Analysis of Air Transportation Network
Bibliographic record
Abstract
World city network formation is one of the most robust trends in the context of globalization. The unprecedented economic transformation and infrastructure restructuring enable China to integrate in world city network overwhelmingly. The purpose of this paper is aiming to conduct a network analysis of Chinese air transportation network based upon large-scale collected data of inter-city air passengers’ volume thereby identifying the world city network of Chinese cities, as well as the internal cooperative relationship and hierarchical structure of these articulations in the network. There are 80 sample cities are enclosed in this air transportation network model using UCINET, which is pioneering social network analytical software. Clearly, UCINET is applied to manipulate the matrix of inter-city air passengers flows in order to elaborate analyze of density of the whole network, to calculate multiple centrality of each node cities, which strives to identify the dominance of each cities’ hierarchical power and positions. In addition, NetDraw program in UCINET is designed to visualize the whole network whereas CONCOR program is operationalized to classify major subgroups within national air transportation network of China. Based on the analysis, we can find that Beijing, Shanghai and Guangzhou play a dominant role in this network, and it is evident that there exist some robust cooperative relationships within and between subgroups arisen from overall air transportation network. Overall, these findings consolidate a concrete cornerstone of Chinese world city network formation.
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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 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| 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) | 0.004 | 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".