Experience and perception of climatic hazards as drivers for adaptation strategies in coastal communities of Bangladesh
Bibliographic record
Abstract
The adoption and implementation of adaptation measures has emerged as a major approach to reducing the impact of climate change and the associated risks to livelihoods. Individual perception is likely to influence considerably the willingness to adopt such measures in response to current and anticipated extreme climate conditions. This study analyzes factors intensifying climatic hazards-induced stress on livelihoods and the underlying motivations driving the adoption and implementation of adaptation measures with an empirical investigation in Bangladesh’s coastal communities of Satkhira district. Two participatory rural appraisal tools—key informant interviews and focus group discussions—were employed for data collection. The findings revealed that cyclones, floods, salinity intrusion, and waterlogging were the primary climate-related hazards experienced by coastal dwellers. Local stakeholders reported that climatic factors coupled with anthropogenic activities resulted in major disruptions to the freshwater supply, causing severe scarcities of water for drinking and irrigation. These experiences and perceptions have effectively motivated locals to adopt and implement adaptation measures, such as crop diversification and the use of climate-smart crop varieties. This research underscores that community engagement, equitable resource distribution, and knowledge enhancement at the community level in policy formulation are critical to achieving the desired outcomes of adaptation.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".