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Record W3100850795 · doi:10.24043/isj.137

Silk Road archipelagos: Islands of the Belt and Road Initiative

2020· article· en· W3100850795 on OpenAlexvenueno aff
Adam Grydehøj, Sasha Davis, Rui Guo, Huan Zhang

Bibliographic record

VenueIsland Studies Journal · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicIsland Studies and Pacific Affairs
Canadian institutionsnot available
Fundersnot available
KeywordsArchipelagoArchipelagic stateGeographyChinaTourismEconomySustainabilityGeopoliticsPolitical scienceEcologyArchaeologyPoliticsLaw

Abstract

fetched live from OpenAlex

The concept behind the Belt and Road Initiative (BRI; formerly ‘One Belt, One Road’) began to take shape in 2013. Since then, this Chinese-led project has become a major plank in China’s foreign relations. The BRI has grown from its basis as a vision of interregional connectivity into a truly global system, encompassing places—including many island states, territories, and cities—from the South Pacific to the Arctic, from East Africa to the Caribbean, from the Indian Ocean to the Mediterranean. Islands and archipelagos are particularly prominent in the BRI’s constituent 21st-Century Maritime Silk Road (MSR) and Polar Silk Road or Ice Silk Road projects, but little scholarly attention has been paid to how the BRI relates to islands per se. This special section of Island Studies Journal includes nine papers on islands and the BRI, concerning such diverse topics as geopolitics, international law and territorial disputes, sustainability and climate change adaptation, international relations of autonomous island territories, development of outer island communities, tourism and trade, and relational understandings of archipelagic networks. Taken together, these papers present both opportunities and risks, challenges and ways forward for the BRI and how this project may impact both China and island and archipelago states and territories.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.426
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
Bibliometrics0.0000.000
Science and technology studies0.0020.001
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 head0.061
GPT teacher head0.317
Teacher spread0.256 · 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.

Study designQualitative
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

Citations9
Published2020
Admission routes1
Has abstractyes

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