The Participation of G20 Countries in Global Value Chains and their Effects on Economic Complexity
Bibliographic record
Abstract
Abstract Today, it is almost impossible for countries to reach a higher level of growth and development just by maintaining their existing production and export structures. Therefore, there has been an increased interest recently in examining the concept of economic complexity in the literature. The foundational premise of these studies is that countries can achieve higher levels of development by producing and exporting more complex products. In this study examines how the integration of various G20 countries into the global value chain affects the economic complexity of these countries. Integration in the global value chain occurs in the form of backward and forward participation. In this context, the study establishes two separate models and explores how these connections affect economic complexity. According to the analysis, GVC participation has a positive effect on the level of economic complexity in China, Korea, Mexico and Türkiye. No significant effect was found in India, Indonesia and Saudi Arabia. In developed countries such as Germany, the US, Australia, France, the United Kingdom, Italy, Japan and Canada the effects of GVC participation were negative. A statistically significant negative effect was also found in developed countries such as Argentina, Brazil, South Africa and Russia.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".