An Empirical Study on the Relationship between Openness and Competitiveness of Maritime Transport Service Trade: Based on Dynamic Panel GMM Analysis
Bibliographic record
Abstract
On the basis of adjusting the openness of maritime transport service trade which is calculated by traditional method and the panel data of 26 maritime countries, 2001~2012, this article does an empirical study on the relationship between the openness and competitiveness of maritime transport service trade with the dynamic panel GMM estimation method. The result shows: the openness and competitiveness of maritime transport service trade is not a simple linear relationship, but presents an inverted U relationship. During the initial period of the process of maritime transport service trade liberalization, a country's competitiveness of maritime transport service trade will be enhanced along with the greater openness. However, when the openness surpasses a certain level, it will have a negative effect on the competitiveness. Therefore, under the guidance of moderate market opening principle of maritime transport service trade, China should enhance the support and protection to maritime transport service trade corporations, and promote their competitiveness in improving the innovation of corporation management.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".