Boom and bust: Soft‐shell mud crab farming in south‐east coastal Bangladesh
Bibliographic record
Abstract
Soft-shell mud crab (Scylla olivacea) farming was introduced to the coastal floodplains of Cox's Bazar, a south-east region of Bangladesh, in 2011. These farming practices then spread among many farmers within the region, and gradually increased in the southwest coastal floodplains of the Satkhira district. While farming in the Satkhira region has experienced relatively smooth growth, the mud crab farms in Cox's Bazar have gone through big ups and downs. Indeed, the number of these farms in the south-east was found to have gradually decreased by 80% in 4 years (2014–2017). This study looks at historical perspectives and thoroughly reviews the state of soft-shell mud crab farming practices. In addition to key stakeholder interviews, data were collected from all the existing and collapsed farms of this type in the Cox's Bazar region. It is revealed that social coherence was the main factor that enabled the ‘boom’ periods due to farmers sharing methods and technologies among their communities. In contrast, poor linkage to global value chains, lack of product diversification, shortage of seed supply due to an inadequate supply of trash fish for feeding, prolonged banning of sea fishing, the poor bargaining capacity of the stakeholders and small-scale farmers lacking direct access to export markets were the key factors for the ‘bust’ periods, even when global demand was increasing daily. A balance between local and global markets, value-added diversified product development and adoption of advanced technologies in the crab farming industry are recommended for the revival and sustenance of this emerging sector.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".