Comparative Advantage and Trade Specialization of East Asian Countries: Do East Asian Countries Specialize on Product Groups with High Comparative Advantage?
Bibliographic record
Abstract
This paper analyzes whether East Asian countries (Indonesia, China, Japan, Hong Kong, South Korea, and Singapore) specialize on product groups with high comparative advantage. We use the data on the 3-digit SITC Revision 2 for 237 product groups published by the UN-COMTRADE. Firstly, we calculate the Revealed Symmetric Comparative Advantage (RSCA) index to know the product groups with high comparative advantage from each the East Asian countries. Secondly, we calculate the export share to know the trade specialization of product groups from each the East Asian countries. Thirdly, we compare between the product groups included in top-twenty SITC of comparative advantage with top-twenty SITC of trade specialization from each the East Asian countries. This paper concludes that throughout the study periods of 1995, 2005, and 2015, East Asian countries (Indonesia, China, Japan, Hong Kong, South Korea, and Singapore) specialize on product groups with low comparative advantage. It was also found that product classification dominating the comparative advantage and trade specialization of East Asian countries was the technology intensive products classification.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".