Link education to industrial upgrading: a comparison between South Korea and China
Bibliographic record
Abstract
Purpose – Why is the “education to industrial innovation” equation not working in China? Why has education development contributed to South Korea’s success but not promoted technology development and industrial upgrading in China? The purpose of this paper is to compare South Korea and China and try to address that puzzle. The author will also identify which mediating factors are crucial in linking education development to industrial innovation and industrial upgrading. Design/methodology/approach – This study will use the historical comparative method to compare South Korea and China. The author will try to explore the differences in education and industrial upgrading in the two countries, and identify which factors are producing different educational development effects, mainly by narrative comparison. Data will mainly come from online databases such as Statistics Korea, the Center on International Education Benchmarking, the UNESCO Institute for Statistics, China Education Statistics and the World Bank, as well as from second-hand resources. Findings – In summary, this research has revealed that education itself or the production of human capital may not be sufficient conditions for technology innovation or industry upgrading. For human capital to affect industrial upgrading positively, it is not enough for the Chinese government just to invest in education. Other intermediating market and social contexts are crucial too, especially the allocation of resources between the private and the public sectors, and the existence of a proper employment structure. Originality/value – The role of education in economic development for the developing world is debated a lot. However, there is little development study research which directly explores the relationship between education and industrial upgrading via macroeconomic analysis. In a globalized world, the situation of international industrial value chains is an important element for sustainable long-term development. Industrial structures and their transformation are becoming more and more important for developing countries. While most past research has treated the absorbing economy’s structure as a condition that determines education’s contribution to development, this paper will treat the industrial structure as the dependent variable, and analyze how education would contribute to the upgrading of industrial structure and, in turn, be of benefit to sustainable economic development.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".