Community-based responses to climate hazards: typology and global analysis
Bibliographic record
Abstract
The severity and frequency of climate change hazards are increasing around the world. Because the impacts are most acutely felt in local communities, it is critical to improve understanding of the response options that are available for and being chosen by communities. We conducted a mixed methods analysis of case studies reporting community-based responses to climate change hazards. Based on content analysis of published case studies, we generated an emergent evidence-based typology of such responses according to their nature and goals. Using this typology, we quantitatively analysed more than 1500 response examples and determined the patterns with which community-level climate change adaptation and disaster mitigation strategies vary across world regions and across economic and governance conditions. Specifically, diversity of responses is lower in developing countries, and implementation of local-level policy and planning responses is less frequent in countries characterised by low governance quality. Our results confirm that, although there is much that local communities can do to respond to the challenges of climate change, there is also a need for increased support of local activities. By synthesising data from many local studies, our research provides a first global evidence base for local-level climate change adaptation policy.
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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 | 0.015 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.017 | 0.017 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".