Technological Regimes, Catching-up and Leapfrogging: Findings from the Korean Industries
Bibliographic record
Abstract
This paper examines the experiences of selected industries in Korea to identify the stylized facts in the process of technological capability building, and thereby, to sort out the conditions for the catching-up to occur. To explain the process, we have built a model of technological and market catching-up. A special attention has been given to the question of whether there has been a case of leapfrogging in any industry in Korea and, if so, what are the conditions for its incidence. In our framework, we first measure the degree of catching-up in terms of market shares in the world. Then, we focus on catching-up in technological capabilities in explaining the different record and prospects of Korean industries in market catching-up. In the model, technological capability is determined as a function of both technological effort and the existing knowledge base. As determinants of technological effort, we look at the technological regimes of the industries, such as cumulativeness of technical advances, fluidity-predictability of technological trajectory, and the properties of knowledge base. Using this model, we explain the different technological evolution of the selected industries in Korea, including the D-RAM, automobile, mobile phone, consumer electronics, personal computer and machine tool industries. We find three different patterns of catching-ups, path-creating catching-up - CDMA mobile phone., path-skipping catching-up D-RAM and automobile., and path-following catching-up - consumer electronics, personal computers and machine tools. We interpret the first two case of catching-up as Aleapfrogging.B Unlike the argument by Perez and Soete wPerez, C., Soete, L., 1988. Catching-up in technology: entry barriers and windows of opportunity. In: Dosi, et al.,Eds.., Technical Change and Economic Theory, Pinter Publishers, London.x, we find that important R&D projects, except automobiles where only private R&D was involved, involved both private and public capacities, and that entry was not driven by endogenous generation of knowledge and skills, but by collaboration with foreign companies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".