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Record W2955892664 · doi:10.1002/tie.22088

Belt‐and‐Road Initiative: Driving the need to understand intellectual capital in Chinese multinational enterprises

2019· article· en· W2955892664 on OpenAlexaff
Eric Kong, William Wei, Phillip Swallow, S. Thomson

Bibliographic record

VenueThunderbird International Business Review · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovation and Knowledge Management
Canadian institutionsCarleton UniversityMacEwan University
Fundersnot available
KeywordsMultinational corporationStrengths and weaknessesBusinessChinaIntellectual propertyCompetition (biology)Intellectual capitalRelational capitalHuman capitalInternational tradeEconomic growthFinancePolitical scienceEconomics

Abstract

fetched live from OpenAlex

Abstract China's Belt‐and‐Road Initiative (BRI) is one of the most ambitious trade and development projects in history which intends to link Chinese multinational enterprises (CMNEs) to the Asian subcontinent, the Middle East, Africa, and Europe through two trade routes, land and sea. The project involves infrastructure development, human knowledge, and international relations to develop trade relationships. Increased competition along the two routes will see other governments taking initiatives to protect the business community in their nations; thus, adding barriers that must be overcome by CMNEs. The success of CMNEs in the BRI relies on the three components—structural, human, and relational—which are the three components of intellectual capital (IC). Through the use of IC CMNEs can assess their strengths and weaknesses. It will be the understanding of these strengths and weaknesses which will drive the success or failure of CMNEs.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.752
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.020
GPT teacher head0.272
Teacher spread0.252 · 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; both teacher heads agree on what is shown here.

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

Citations8
Published2019
Admission routes1
Has abstractyes

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