Navigating Vulnerabilities and Responses for Transitioning to Viability in the Small-Scale Fisheries of Bangladesh
Bibliographic record
Abstract
This meta-analysis examines 97 case studies to synthesize vulnerabilities and responses within small-scale fisheries (SSF) across seven aquatic ecosystems in Bangladesh, revealing multifaceted threats in social, economic, biophysical, governance, and technological contexts. Small-scale fisheries in coastal environments represent the highest vulnerabilities, followed by riverine, floodplain, mangrove, marine, estuarine, and lake ecosystems. Responses to these vulnerabilities are complex and multifaceted, and shaped by short-term and long-term strategies. Individual, household, or family-level responses are more common than those taken collectively at the state, community, society, or agency levels. Reliance on social networks and food aid are frequently used short-term responses, while governance strategies and capacity development are common long-term responses. This study offers novel understanding of vulnerabilities and responses of SSF communities in Bangladesh, and emphasizes the need for comprehensive policies, multilevel governance and community actions that can facilitate strengthening of resilience of SSF communities and their transitions to viability in Bangladesh.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 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".