Beyond the vulnerability/resilience dichotomy: Perceptions of and responses to the climate crisis on Emau, Vanuatu
Bibliographic record
Abstract
In Vanuatu, a South Pacific island nation, the effects of climate change pose new challenges for low-lying coastal communities. This study explores how one village on Emau, an island offshore of capital island Efate, has developed several overlapping strategies to manage climate change impacts, including drought and sea level rise. Informants reveal their perceptions of changing environmental baselines and how socio-economic processes, including population growth, cultural loss, and limited access to cash incomes, have shaped the community’s response. Informants describe four climate adaptation strategies: 1) expanding access to cash income through seasonal or urban labor migration; 2) leveraging international expertise and funding to meet their goals; 3) developing hybrid forms of traditional practices and contemporary ideology to preserve environmental knowledge; and 4) performing physical and emotional labor to preserve and remain on their land. These strategies span oceans and cross international borders, refuting narratives of islands’ being ‘isolated’ from the rest of the world and passive ‘victims’ of climate change. Contextualizing perceptions of and responses to environmental change provides critical nuance to the resilience/vulnerability framework, which alone obscures ongoing political, social and economic processes on islands.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".