China's Trends in Provincial Logistics Based on Railway Transportation Data
Bibliographic record
Abstract
This study uses the railway transportation data of China to analyze trends in provincial logistics. In particular, the railway O-D (Origin and Destination) table (formally titled “ Freight Exchange of National Railway between Administration Regions” ) in the “ Year Book of China Transportation and Communications” is the only material that supplements provincial logistics in China.First, the study calculates the shares among provinces. Second, the study estimates the future distribution by stochastic models represented by the Markov chain. Third, the study suggests a simple indicator that analyzes the changes in shares. According to this indicator, 0% shows no change in shares, whereas 100% show that share changes from one side to another. These results clearly indicate the trends and patterns in provincial logistics change slowly, resulting in less than 10% share change and stabilization of future convergence distributions.Therefore, few changes can be expected in the provincial logistics trends in China However, this study is limited by the data obtained, because it does not analyze other modes of transportation. If the trends in logistics do not change through time, it is difficult to suggest a logistic policy, especially in terms of railway transportation, to reduce regional disparity. The policy for constructing a railway logistic center in poor regions to reduce disparity is not realistic. On the other hand, the demand for railway construction based on actual demand will continue for a while. As a result, there is a possibility the logistic policy will be influenced against our expectations if the trends in logistics greatly change. Therefore, a logistic policy for economical reasons is indispensable.JEL Classification: C49, O53, R49
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.000 | 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".