Mapping Social-Ecological-Oriented Dried Fish Value Chain: Evidence from Coastal Communities of Odisha and West Bengal in India
Bibliographic record
Abstract
The production and trade of dried fish are important sources of livelihood and employment for poor people engaged in the dried fish value chain. More importantly, half of them are women. Dried fish makes a significant contribution to the food and nutrition security of the poor because it is high in calcium and other vital micronutrients. Despite its importance, work on the dried fish value chain (DFVC) continues to focus on financial value creation and linear interactions among market actors that impede the recognition of human rights, justice, food security, and power across the entire value chain. Such a neoclassical perspective on DFVC tends to undermine the complex human-nature interactions that are contingent upon specific histories, people, places, and practices. Poor fishers and dried fish processors placed at the extractive end of the value chain hold low power in the market and remain vulnerable to changing social-ecological system dynamics. The recent work on a hybrid framework of social-ecological system-oriented dried fish value chain (SESDFVC) makes a departure from the conventional dried fish value chain framework. It values dynamic resource contexts, considers upstream actors as active collaborators, and expands the notion of value to include the social-ecological wellbeing of the value chain actors. This paper, with a mixed method research framework, provides an empirical outlook of the dried fish value chain in relation to SES attributes in the context of the eastern Indian coast of the Bay of Bengal, including Odisha and West Bengal, India.
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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 | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| 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".