Island climate change adaptation and global public goods within the Belt and Road Initiative
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".