LARGE AND LATECOMER FIRMS: THE TAIWAN SEMICONDUCTOR MANUFACTURING COMPANY AND TAIWAN'S ELECTRONICS INDUSTRY
Bibliographic record
Abstract
ABSTRACT This paper focuses on the role of Taiwan's ‘latecomer firms’ that are also large firms in developing technological capability within the electronics industry. Taiwan illustrates a latecomer country that has industrialised in the late twentieth century through export‐based industrialisation and latecomer firms are indigenous influences shaping this process. Large firms comprise a business segment distinctive from small firms and giant MNCs, and are characterised by strong commitments to innovation. Conceptually, a framework is outlined that connects latecomer firms with the triad business segmentation model, local technology learning as summarised by reverse product cycle dynamics and clustering. Empirically, the study focuses on the evolutionary dynamics of a latecomer case study, the Taiwan Semiconductor Manufacturing Company, to reveal important insights regarding the development of Taiwan's internationally competitive technological capabilities. The case study reveals the significance of large latecomer firms to the technology learning process, and in reducing technology gaps with global leaders.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".