An Empirical Study on International Trade Pattern of Products in Aviation and Aerospace from 2002 to 2012 Based on Social Network Analysis
Bibliographic record
Abstract
Based on the bilateral trade data in the aviation and aerospace industry among the 50 important economies every year during 2002—2012,the paper studies the international product trade pattern in aviation and aerospace by the method of social network analysis. The results show that the trade network gets gradually denser,and more and more economies construct two- way trade relationships between each other. France,USA,Germany and Canada are the hubs of the trade networks,however their control forces are on the decline. Meanwhile,emerging economies have advanced,such as China and Brazil. The economic crisis since 2008 has an impact on the trade network,especially developed countries. However,depending on the stable trade volume,particularly trade value between them,France and Germany grow into the absolute core of the trade network. Although China has achieved the promotion of the trade status,it should still develop the key technologies and products with independent property rights in the aviation and aerospace industry,and hence reduce the ratio of dependence on import. In addition,in the research on the trade network,the method of social network analysis should be integrated with the connotation of network indices and the original trade data.
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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 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| 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".