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Record W3036017678 · doi:10.5430/rwe.v11n3p1

A Comparative Study of the Development of Technology-Intensive Industries: Korean and Romanian Automobile Industries

2020· article· en· W3036017678 on OpenAlexvenueno aff
Jae Min Kang, Jai S. Mah

Bibliographic record

VenueResearch in World Economy · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicInnovation Policy and R&D
Canadian institutionsnot available
Fundersnot available
KeywordsAutomotive industryIndustrialisationBusinessGovernment (linguistics)Linkage (software)Industrial organizationRomanianComparative advantageEconomicsInternational tradeEngineeringMarket economy

Abstract

fetched live from OpenAlex

The automobile industry is a technology-intensive industry that has the potential to create successes in many other related industries. This paper analyzes the approaches taken during the developmental processes of the automobile industries in Romania and Korea. This paper identifies the causes of their rapid growth and compares the experiences of the two nations particularly with respect to the approaches they took during the developmental stages of their automobile industries. Automobile industries in these two countries, Dacia and Hyundai in particular, contributed to economic growth in terms of employment, income generation and development of advanced technologies via the linkage effect. After comparing the merger and acquisition approach in Romania with the technology licensing approach in Korea in light of the government policies which pursued industrialization since the 1960s, it provides policy implications for developing countries which try to develop technology-intensive industries such as the automobile industry.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.299
Threshold uncertainty score0.372

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.232
GPT teacher head0.353
Teacher spread0.121 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations1
Published2020
Admission routes1
Has abstractyes

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