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Record W2998950619 · doi:10.5430/jms.v11n1p1

Does Saudi Arabia Benefit From China’s Belt and Road Initiative?

2019· article· en· W2998950619 on OpenAlexvenueno aff
Khaled Mohammed Alqahtani

Bibliographic record

VenueJournal of Management and Strategy · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicMiddle East and Rwanda Conflicts
Canadian institutionsnot available
FundersGovernment of Jiangxi ProvinceChina Railway
KeywordsChinaMiddle EastPolitical scienceIdeologyGeographyBusinessEconomic growthEconomyPoliticsEconomicsLaw

Abstract

fetched live from OpenAlex

The People's Republic of China and the Kingdom of Saudi Arabia enjoy close and friendly relations and share broad common interests-regardless of their ideological differences, evident in their names. China’s Belt and Road Initiative is aimed at developing infrastructure projects and declining transportation costs to interlink cross-border trade deals between China and countries along the routes. The Kingdom of Saudi Arabia, a central hub connecting Asia, Africa and Europe, has been a significant part of the initiative. Moreover, Saudi Arabia is one of the first countries that have responded positively to the Belt and Road Initiative (BRI). The BRI brings China much closer to Saudi Arabia. As BRI’s linchpin in the Middle East, dose Saudi Arabia benefit from this initiative? Based on the elaborate analysis of main research question, this study reveals that the BRI offers great potential opportunities for the Kingdom in terms of infrastructure construction, energy cooperation, technology and finance, culture exchange, security and defense. In addition, China could collaborate with Saudi Arabia in a significant number of sectors where Saudi and Chinese strengths are complementary. The deeper Sino-Saudi cooperation can also brighten the prospects for Saudi Vision 2030.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.675
Threshold uncertainty score0.423

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.020
GPT teacher head0.263
Teacher spread0.243 · 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

Citations4
Published2019
Admission routes1
Has abstractyes

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