The Impact of Standard on the Competitiveness of Transportation Service Trade: An Empirical Analysis Based on Chinese Whole and Sectoral Panel Data
Bibliographic record
Abstract
This article applies cointegration and Granger methods to demonstrate the relation between Chinese transportation industry standard and transportation service trade competitiveness, constructs multiple regression model, and analyzes the relation among standards of different transportation departments(shipping, air cargo and other transportation) and their competitiveness. This article gets the following conclusions: Chinese transportation standards have actively simulative effect on its trade competitiveness; standards of different transportation departments have different effect on transportation service trade, and sea transportation sectoral standard has the most effect on transportation service trade; the effect of transportation standards on the service trade competitiveness of different transportation sectors is different. The effect of transportation standard on the trade competitiveness of sea transportation department is most obvious; the research, which takes FDI as the substitute index of Chinese transportation market opening extent, finds that the increasing of transportation market opening can not improve Chinese transportation service competitiveness and has adverse influence on it.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".