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Record W3176264954 · doi:10.1080/09537325.2021.1947487

Global value chain embeddedness and innovation efficiency in China

2021· article· en· W3176264954 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueTechnology Analysis and Strategic Management · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal trade and economics
Canadian institutionsMemorial University of Newfoundland
FundersNational Natural Science Foundation of China
KeywordsGlobal value chainEmbeddednessValue (mathematics)ChinaBusinessPosition (finance)Value chainAffect (linguistics)Chain (unit)Human capitalStochastic frontier analysisIndustrial organizationFrontierEconomic geographyEconomicsSupply chainMicroeconomicsEconomic growthMarketingInternational tradeComparative advantageProduction (economics)Geography

Abstract

fetched live from OpenAlex

Combined with the global value chain (GVC) and the innovation value chain, this study analyses whether and how global value chain participation (GVCPA) and global value chain position (GVCPO) affect innovation efficiency (IE) to explain the difference of IE in China’s provinces from 2005 to 2016. It uses Stochastic Frontier Analysis to examine the influencing factors of IE. Results show that both GVCPA and GVCPO are important factors influencing IE, with GVCPA promoting IE and GVCPO inhibiting IE. Moreover, human capital (HC) positively affects the relationship between GVCPO and commercialisation efficiency as well as the relationship between GVCPA and R&D efficiency. The findings suggest that a region should not only encourage industrial enterprises to be embedded in the GVC but also develop education and improve the quality of HC.

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.

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)0.000
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.109
Threshold uncertainty score0.589

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.027
GPT teacher head0.230
Teacher spread0.203 · 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