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Record W3016449801 · doi:10.1002/tie.22134

Assets of origin? Chinese multinational enterprises amidst the Belt and Road Initiative

2020· article· en· W3016449801 on OpenAlexaff
Liang Wang, Haifeng Yan, Xiaohua Yang, Francesco Ciabuschi, William Wei

Bibliographic record

VenueThunderbird International Business Review · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInternational Business and FDI
Canadian institutionsMacEwan University
Fundersnot available
KeywordsMultinational corporationForeign direct investmentBusinessEmpirical researchInvestment (military)International tradeEconomicsFinancePolitical sciencePoliticsMacroeconomics

Abstract

fetched live from OpenAlex

Abstract This article reviews the current literature on the implications of the Belt and Road (B&R) Initiative for Chinese multinational enterprises (CMNEs) and calls for further empirical investigations of the motivations, processes, and consequences of the expansion of CMNEs into B&R countries. We posit that the rapid expansion of CMNEs in these countries indicates assets, rather than liabilities, for the county of origin. Empirical studies in this special issue provide new insights into what is “Chinese” about Chinese foreign direct investment in B&R countries and how the “assets of origin” may play a role in CMNEs' expansion in B&R countries.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.631
Threshold uncertainty score0.872

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
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 head0.038
GPT teacher head0.293
Teacher spread0.255 · 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

Citations10
Published2020
Admission routes1
Has abstractyes

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