The role of logistics performance and decreasing of trade competitiveness in ASEAN+3’s manufacturing products
Bibliographic record
Abstract
ASEAN Countries members plus Japan, South Korea, and China (ASEAN+3’S) logistics performance play a significant role in maintaining and improving their export and import values, depending on the various commodities trade. Meanwhile, during uncertain situations today, the stakeholders need to enhance the capability of import and export activities to improve logistics performance. The study focused on the competitiveness of the manufacturing products traded by these countries. The study used panel data analysis based on the panel data of 11 years (2008-2018) from the 10 ASEAN+3 countries. Export value and Net Comparative Advantage (NCA) index (including net export and trade openness) were used as dependent variables in the two model studies, and logistics performance was the primary variable. The result shows that logistics performance positively affects the export and trade competitiveness models of ASEAN+3’s manufacturing products. The Logistics Performance Index (LPI) provides estimates that suggest that logistics performance has a significant impact on ASEAN + 3 export value (ExpM) and manufacturing trade competitiveness (NCAM). Meanwhile, there are different results of the effects of macroeconomic variables between the model of export value (ExpM) and the NCAM in the manufacturing in ASEAN+3’s. The ExpM model follows the theory that Gross Domestic Product (GDP) and Foreign Direct Investment (FDI) increase the competitive value in ASEAN+3 countries. Meanwhile, in the NCAM model, GDP and FDI reduce trade competitiveness because of the high value of ASEAN+3 manufacturing imports.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 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".