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LARGE AND LATECOMER FIRMS: THE TAIWAN SEMICONDUCTOR MANUFACTURING COMPANY AND TAIWAN'S ELECTRONICS INDUSTRY

2009· article· en· W2082546707 on OpenAlexafffund
Chia-Wen Lee, Roger Hayter, David W. Edgington

Bibliographic record

VenueTijdschrift voor Economische en Sociale Geografie · 2009
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal trade and economics
Canadian institutionsUniversity of British ColumbiaSimon Fraser University
FundersSocial Sciences and Humanities Research Council of CanadaTaiwan Semiconductor Manufacturing Company
KeywordsBusinessIndustrial organizationIndigenousMultinational corporationIndustrialisationProcess (computing)Developing countryElectronicsEconomic geographyCommerceEconomicsMarket economyEconomic growth

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.765
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.022
GPT teacher head0.218
Teacher spread0.195 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations11
Published2009
Admission routes2
Has abstractyes

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