Addressing Wetland Flood Disasters Through Community-led Strategies in Bangladesh
Bibliographic record
Abstract
This study focused on mitigating wetland flood disasters in Bangladesh through community-led strategies, particularly in land-based minority communities. Wetland ecosystems, integral to the country’s landscape, are increasingly vulnerable to floods exacerbated by climate change. Recognising the intersectionality of environmental challenges and community well-being led to proactively addressing the impacts of wetland floods. This study uses participatory methods to engage minority communities, particularly those in the wetland regions. Focusing on local community-engaged approaches, the research aims to develop community-led adaptive strategies. The study emphasises the active participation of community members in decision-making processes through a community-led approach, enhancing resilience and sustainability. The study also explores the role of women in these community-led initiatives, acknowledging their unique perspectives and contributions to adaptive strategies. Ultimately, the findings aspire to inform policy frameworks and global discourse on disaster resilience, offering insights into how community-led strategies can serve as effective models in mitigating the impact of wetland flood disasters and foster a sense of hope and optimism for the future.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".