Use Of Pesticides Or Harmful Substances In Fish Drying: A Study On The Coast Of Purba Medinipur District, West Bengal
Bibliographic record
Abstract
Fishing is one of the major livelihood activities of traditional fishermen in the coastal region of Purba Medinipur district in West Bengal. Most of these traditional fishermen are lower-caste Hindus. In the traditional caste hierarchy, these traditional Hindu fishermen face various kinds of discrimination and unlawful activities. Poverty is their common feature. A large number of traditional fishermen depend on the traditional petty trade of fish drying. Earlier, they used only traditional technologies and did not depend on harmful substances (especially various pesticides or insecticides) to dry or preserve fish in a short time. Recently, the technology and methods of drying fish have changed a lot for fishermen in coastal regions. Nowadays, traditional fishermen often use various harmful substances such as pesticides or insecticides to dry the fish quickly, to preserve it for a longer period and to dry it during the rainy season or in other humid climatic conditions. Most of these substances have serious health impacts. The present work attempted to determine the health impacts of fish drying workers who work in khoti areas (fish drying centres where fishermen are engaged in fish drying) and are involved to some extent in this chemical-driven fish drying process. The study was conducted among the traditional fishermen engaged in fish drying and petty trading in different khoti areas of Purba Medinipur district in West Bengal. It is mainly a field observation-based work in which various traditional methods of fieldwork were used with due importance
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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".