Nexus Between Vulnerability, Livelihoods and Non-Migration Strategies Among the Fishermen Communities of Sundarbans, Bangladesh
Bibliographic record
Abstract
This article investigates the reasons behind the non-migration of fishermen communities living adjacent to the Sundarbans in Bangladesh. In addition to the livelihood strategies of these communities living in the southern districts of the country, this article explores a symbiotic relationship among livelihoods, risks and natural resources in understanding the fishermen’s choice of locations in these vulnerable areas. We have adopted a mixed scientific approach method in collecting, analysing and summarizing obtained information. We have employed a triangulation schema in the study, that is, collected data from multiple sources to compare and use relevant methods to check consistencies. On analysing the data collected from field investigation, it can be concluded that a critical relationship exists among livelihoods, risks and the immobility of the fishermen community in the Sundarbans. Their non-migration can be explained between voluntary and non-voluntary movements depending on livelihoods, vulnerability and available resources. The findings reveal that households seek to mobilize resources and opportunities to combine them into a livelihood strategy which includes the following: (a) natural resource extraction; (b) diversified income generation; (c) borrowing and investment; (d) labour and asset pooling; and (e) social networking. Finally, this study concludes that this process of combining and transforming different assets for livelihood strategies can be explained as an autonomous adaptation process.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".