‘Sufferings Start from the Mothers’ Womb’: Vulnerabilities and Livelihood War of the Small-Scale Fishers of Bangladesh
Bibliographic record
Abstract
Due to its deltaic geographical position and precarious socioeconomic and demographic conditions, Bangladesh is recognized worldwide for its exposure to recurring environmental hazards. Based on a 21-month long field study in two fishing villages that are characterized by distinct ecological settings and ethnic groups, this article examines the arrays of cross-scale environmental, social and institutional stressors that singly or cumulatively impact fishers’ livelihood well-being and generational poverty. Analysis of the vulnerabilities makes it clear that the degree to which poor fishers suffer from environmental stressors and calamities is determined not only by the frequency of abnormal events, but also by their internal capabilities of self-protection, resilience against those stressors, position in the social network and asset and resource ownership. Coastal and floodplain fishers identified cyclone and long-standing floods as strong drivers of poverty as their bundles of ‘safety net’ capital are usually disrupted or lost. For a majority of the fishers, income/day/family declines to as low as US$ 0.7–0.9. Fishers lack appropriate sets of endowments and entitlements that would allow them immediate buffer against livelihood stressors. Vulnerability here is intricately related to one’s socio-economic status; poor and ‘socially vulnerable’ ethnic fishers are concurrently ‘biologically vulnerable’ too. The corollary of multi-faceted stressors is that, poverty persists as an ever-increasing haunting presence that thousands of floodplain and coastal fishers of Bangladesh are forced to cope with. It is evident that nature-induced stressors exert ‘ratchet effects’ on fishers with low endowments who critically risk nutritional deprivation and social standing. Lucidly, most of the fishers are trapped in a form of ‘livelihood war’.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".