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Record W4384297110 · doi:10.46281/aijmsr.v14i2.2050

SOCIAL, ECONOMIC, AND ENVIRONMENTAL IMPACTS OF THE ONE BELT ONE ROAD INITIATIVES

2023· article· en· W4384297110 on OpenAlexaff
Rajarshi Roy Chowdhury, Debashish Roy, Md Mamunur Rashid, Md Sumon Reza

Bibliographic record

VenueAmerican International Journal of Multidisciplinary Scientific Research · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicBelt and Road Initiative
Canadian institutionsToronto Metropolitan University
FundersUniversiti Brunei Darussalam
KeywordsSustainabilityChinaSustainable developmentGlobalizationPolitical scienceHumanityBusinessEconomic growthEconomicsEcology

Abstract

fetched live from OpenAlex

The One Belt One Road Initiative (OBOR) by China presents a grand vision to the world that aims to foster cooperation among different countries in various fields such as global trade, international relations, infrastructure development, education, and technology. Also known as the Belt and Road Initiative (BRI), it comprises a network of roads, railways, and sea routes, all geared toward the development of humanity. The purpose of this research is to analyze some of the significant impacts of this massive project in terms of social, economic, and environmental aspects. Through the exchange of culture, sports, education, and international relations on six economic corridors, the BRI has the potential to create substantial economic benefits. The project also prioritizes environmental sustainability through a green BRI approach. All the quantitative and qualitative data are extracted from different research papers/reports, published books, and some online based data portals. It has been shown that the OBOR/BRI seeks to connect the world and foster peace, whilst its implementation may face significant challenges. Nonetheless, it presents new opportunities for people and may usher in a new era of globalization.

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)0.003
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.143
Threshold uncertainty score0.787

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.002
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.116
GPT teacher head0.372
Teacher spread0.257 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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