Community-based adaptation to climate change: lessons from Tanna Island, Vanuatu
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| 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".