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Record W3101510127 · doi:10.24043/isj.134

Island climate change adaptation and global public goods within the Belt and Road Initiative

2020· article· en· W3101510127 on OpenAlexvenueno aff
Chunlin Li, Adam Grydehøj

Bibliographic record

VenueIsland Studies Journal · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicIsland Studies and Pacific Affairs
Canadian institutionsnot available
FundersFujian Provincial Federation of Social Sciences
KeywordsClimate changeAdaptation (eye)Public goodGlobal public goodCorporate governancePolitical scienceSustainable developmentEnvironmental resource managementBusinessEnvironmental planningGeographyEconomicsEcology

Abstract

fetched live from OpenAlex

The Belt and Road Initiative (BRI), a project conceptualized and developed by the Chinese state, aims to enhance international cooperation, address issues of shared regional and global concern, and create opportunities for foreign direct investment in struggling economies. The BRI can be seen as a system for supplying global public goods, including sustainable development within which issues related to climate change sit. A great many small island states and territories are participating in the BRI, particularly in its constituent 21st-Century Maritime Silk Road. However, the BRI has not yet placed sufficient focus on climate change adaptation or issues specific to small islands. Furthermore, the BRI’s conceptual basis in rhetoric of mutual dependence and a community of common destiny have not always been evident in the individual activities that have been carried out within the BRI. If the BRI’s goals are to be taken seriously, it must do more to focus on the needs and perspectives of island communities, particularly with regard to climate change adaptation. This paper presents a framework for action to strengthen the BRI’s approach to islands and climate change adaptation in terms of information sharing, scientific and technological cooperation, financial support, and capacity building within a global governance framework.

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)0.000
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 score0.586
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
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 head0.152
GPT teacher head0.339
Teacher spread0.186 · 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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