What can be said about risks, vulnerabilities, and adaptation to climate change in Caribbean small island developing states (SIDS)? The case of Dominica. A qualitative study
Bibliographic record
Abstract
Caribbean Small Island Developing States (SIDS) are qualified as disproportionately vulnerable to climate change, including climate extremes like hurricanes. Yet, there is a paucity of research regarding climate risks that refer to health and human mobility, and there is a need for vulnerability and adaptation assessment in Caribbean SIDS. Our study discusses risks and vulnerabilities including local adaptive capacity in a Caribbean context to inform future adaptation measures to climate change. Our discussion is based on qualitative data collected in the Caribbean islands of Dominica and Guadeloupe. The data emanates from semi-structured interviews organized between March 2020 and January 2021 with people who were either displaced within Dominica following climate extremes that struck Dominica in 2015 (Tropical storm Erika) and 2017 (Hurricane Maria), and also with people who migrated to Guadeloupe in 2017. Interview guides were based on conceptual frameworks on climate change, migration and health, and vulnerability to climate change. Data was analyzed deductively, based on frameworks and inductively to allow new codes to emerge. Participants demonstrated diverse perspectives on climate change. The study highlighted the significance of social ties and resources in supporting local adaptive capacity and mobility in response to climate extremes. Agriculture’s vulnerability raised concerns for long-term economic implications and food security. Some recommendations for building local adaptive capacity to climate change underscore the need for knowledge and information exchange between actors and institutions, and community inclusion; equity; enhanced coordination between government and local actors and decentralisation; and public health programmes and resources. Institutions such as health, education and media should be strengthened to build adaptive capacities for communities in the face of climate change.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.017 | 0.016 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".