Linking the technological regime to the technological catch-up: analyzing Korea and Taiwan using the US patent data
Bibliographic record
Abstract
This article examines the relationship between the technological regime and the technological catch-up, using US patent data. This study first extends the notion of technological regimes as more appropriate for the catching-up context before it goes on to develop the quantitative expressions of technological regime variables. Then, it investigates in which technological classes technological catch-up tends either to occur or not to occur and what affects the speed of the catch-up. This study has found that catching-up is more likely to happen in those technological classes with shorter technological cycle time and more initial stock of knowledge and that among those candidate classes the actual speed of catch-up varies depending on appropriability and knowledge accessibility. This implies that the factors that determine the occurrence of catch-up and the speed of catch-up are different. Comparing the level of technological capability of the advanced and catching-up economies, the article has found that catching-up countries tend to achieve high levels in the technological sectors with shorter cycle time, easier access to knowledge, and higher appropriability, whereas the advanced countries show the exactly opposite performances. The study also confirms the organizational selection hypothesis such that the firms of different organizations and strategies show divergent degrees of fitness in the different environment or technological regime. We find that the Korean firms find themselves more fitted to technological regimes featured by low appropriability and high cumulativeness (persistence), whereas the Taiwanese firms are more fitted to technological regimes featured by high appropriability and low cumulativeness (persistence). Our findings are consistent with the following characterization of the firms in Korea and Taiwan. The Korean firms, dominated by the so-called Chaebols especially in patent registrations, are characterized as less flexible, large diversified conglomerates and pursing more independent R&D and learning strategies. The Taiwanese firms are characterized as more flexible, network-based, specialized firms and pursuing more cooperative R&D and learning strategies.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".