China's Trends in Provincial Logistics Based on Railway Transportation Data
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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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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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