World City Network in China: A Network Analysis of Air Transportation Network
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it