Water, people and climate-change exposure in the Western Pacific: Anticipating the arrival of a ‘perfect storm’
Bibliographic record
Abstract
As climate change accelerates, there is a growing need to ensure that sustainable adaptive solutions are effective and equitable, especially in the Global South where many countries depend on external funding to attain water security. Perceptions of vulnerability and need among Pacific Island Countries are not always based on a region-wide evidence base. This study examines ten Western Pacific Island countries (Federated States of Micronesia [FSM], Fiji, Kiribati, Marshall Islands, Nauru, New Caledonia [French dependency], Palau, Solomon Islands, Tuvalu, Vanuatu) that include both high-island groups and low-island (atoll) groups. This study evaluates the equitability of the distribution of external funding for attaining water security. Needs are evaluated in terms of (a) population densities and growth compared with water and land availability and (b) the uneven distribution of water-focused livelihood stressors across this region, specifically those linked to climate variability, sea-level rise, tropical cyclones, and geophysical phenomena. Measures of comparative exposure of people in these countries show that those living along high-island coasts, especially in Fiji, Solomon Islands and Vanuatu, are considerably more exposed than their counterparts elsewhere, especially in atoll nations which have received greater amounts of per capita climate funding for water security. Results show that there is a ‘perfect storm’ brewing in the high-island nations of the Southwest Pacific resulting from their comparatively high exposure to livelihood stressors. This could be addressed by reassessing the distribution of external climate funding within the Western Pacific region. Key findings are the importance of aligning need with assistance and the foundational role of water in livelihood sustainability, both having implications for the hundreds of coastal communities forced to relocate, mostly locally on the same island, in the next few decades. The imperative of addressing such deficiencies in an era of accelerating climate change and declining levels of global support is clear.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".