SHRIMP FARMERS’ COMPETENCE AND TRAINING NEEDS ON CLIMATE CHANGE ADAPTATION: A CASE STUDY FROM SOUTHWEST COASTAL BANGLADESH
Bibliographic record
Abstract
Sustainability of shrimp farming is important for both environmental and economic benefits of Bangladesh.The Department of Fisheries provides training to the shrimp farmers; however, techniques on adaptation to climate change are not adequately addressed in the training.This study assessed the competence and training needs of shrimp farmers on climate change adaptation.Two groups of shrimp farmers (each group consisting of 50 individuals) were surveyed and 20 key informants were interviewed from Kaikhali and Ramjannagar unions of Shyamnagar subdistrict under Satkhira district in southwest Bangladesh.Group A included shrimp farmers who had recently participated in training, and group B (control group) included shrimp farmers who had never attended a similar training and had no contact with the shrimp farmers of Group A. Borich Needs Assessment Model was used to assess the training needs of the shrimp farmers.Both of the groups imposed high importance on the skills for adaptation to climate change, but Group A had high competence and Group B had moderate competence.The top three training needs for both of the groups were: a) controlling fluctuation of salinity, b) management for heavy rainfall, and c) management for drought.Shrimp farmers of southwest coastal Bangladesh need more training support for adaptation to climate change.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".