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Record W4390894665 · doi:10.22452/ijcs.vol14no2.7

Making Middle-Power Alignment Work: Reinforcing Taiwan-Vietnam Collaboration in the Semiconductor Industry

2023· article· en· W4390894665 on OpenAlexaboutno aff
Huynh Tam Sang, VO Thi Thuy An

Bibliographic record

VenueInternational Journal of China Studies · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicAsian Industrial and Economic Development
Canadian institutionsnot available
Fundersnot available
KeywordsSemiconductor industryChinaGeopoliticsScholarshipMiddle EastPolitical scienceCompetition (biology)Power (physics)Economic growthWork (physics)EconomyEngineeringEconomics

Abstract

fetched live from OpenAlex

Through their joint initiatives, emerging middle powers are taking on a bigger role in the Indo-Pacific. But current scholarship on middle powers mainly focuses on countries with well-established reputations, such as Australia, Canada, South Korea, and Japan. Taiwan and Vietnam are two prime examples of emerging middle powers whose role and contributions have been under-examined. The authors contend that, against the backdrop of US-China technology competition, Taiwan and Vietnam should enhance collaboration in the semiconductor industry in an effort to forge closer ties and navigate geopolitical shoals and reefs, leading to the development of a more resilient semiconductor value chain. This paper discusses Taiwan’s crucial role as a potent player in the global semiconductor business in addition to presenting Vietnam’s aspirations to become Southeast Asia’s hub

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0110.004
Scholarly communication0.0070.005
Open science0.0010.011
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.001

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 head0.117
GPT teacher head0.389
Teacher spread0.272 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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

Citations2
Published2023
Admission routes1
Has abstractyes

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