Analysis of the Spatial Pattern and Structure of the Global Cross-Border Logistics Network
Bibliographic record
Abstract
Under the background of fierce competition in global cross-border e-commerce, cross-border logistics has become the most important and weakest link in the whole industrial chain. Based on data mining and tracking of 357 cross-border logistics channels and 380 million waybills, this paper constructs a global cross-border logistics network with 213 countries and regions as nodes, and conducts network analysis and geographical interpretation. The results show that: (1) At present, the global cross-border logistics development has become more mature, and its overall spatial pattern presents a hub-and-spoke structure with China as the outflow core and the US as the inflow core. The connections between nodes are imbalanced, and for most countries and regions, inbound flows exceed outbound flows. (2) The global core hubs are China, the United States, the United Kingdom and Hong Kong SAR, China, followed by Germany, France, Canada and India. (3) The entire network is highly connected, showing an obvious core-edge layer structure, without obvious small groups. (4) The core circle layer is China and the United States, while the half-edge circle layer and the edge circle layer contain 85 and 126 countries and regions, respectively. The overall flow direction between the circles presents a diffusion state from the inside to the outside. The long-tail effect is significant, and the regionalization degree of the edge circle is higher than that of globalization.
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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 | 0.008 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".