Community-based adaptation to climate change: lessons from Tanna Island, Vanuatu
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Community-based adaptation has gained significant international attention as a way for communities to respond to the increasing threats and complex pressures posed by climate change. This bottom-up strategy represents an alternative to the prolonged reliance on, and widespread ineffectiveness of, mitigation methods to halt climate change, in addition to the exacerbation of vulnerability resulting from top-down adaptation approaches. Yet despite the promises of this alternative approach, the efficacy of community-based adaptation remains unknown. Its potential to reduce vulnerability within communities remains a significant gap in knowledge, largely due to limited participatory evaluations with those directly affected by these initiatives, to determine the success and failure of project design, implementation, outcomes and long-term impact. This paper seeks to close this gap by undertaking an in-depth evaluation of multiple community-based adaptation projects in Tanna Island, Vanuatu and exploring community attitudes and behavioural changes. This study found that future community-based adaptation should integrate contextual specificities and gender equality frameworks into community-based adaptation design and implementation, as well as recognise and complement characteristics of local resilience and innovation. In doing this, the critical importance of looking beyond assumptions of Small Island Developing States (SIDS) as homogenous, primarily vulnerable to climate change and lacking resilience, was also recognised.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it