Wild Shrimp Larvae Harvesting in the Coastal Zone of Bangladesh: Socio-economic Perspectives
Bibliographic record
Abstract
About 0.42 million people are involved in shrimp post larvae collection along the estuaries and coastline of the Bay of Bengal in Bangladesh. Shrimp fry collection from wild sources has assumed a notorious image for being ecologically destructive. In 2000, the Government of Bangladesh imposed regulation to stop shrimp seed collection to protect the fisheries resources. But thousands of people involved in post larvae collection are defying the ban. There is an apprehension that strict implementation of the banning ordinance may displace the people who depend upon the income from catching the larvae. To get the socioeconomic patterns of fry collection 72-85 collectors were interviewed weekly from three harvesting sites. This paper analyzes the larvae collection and distribution efficiency, livelihood strategy of fry collectors, user options for fisheries management and role of various stakeholders empirically. Results show that poverty, migration, credit systems and lack of coordination of service-providing agencies all have important influence on shrimp fry collection in the coastal zone. With an ever-increasing demand for sustainable use of coastal fisheries resources there is a need for consensus among the stakeholders. We propose alternative employment opportunities for fry collectors, community participation and integrated coastal zone management approach for the development of fisheries resources.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".