Gendered dimensions of social wellbeing within dried fish value chains: insights from Sri Lanka
Bibliographic record
Abstract
How small-scale fishers participate in and benefit from land-based fish drying and processing activities is rarely documented and poorly understood. This paper aims to address this gap by bringing attention to dried fish value chains (DFVC), which comprise activities from fish harvesting to drying/processing and trading. We draw on value chain and social wellbeing literatures and adopt a case study approach to examine urban coastal and rural inland DFVCs in Sri Lanka. Our results emphasize the nuanced and unique ways in which people derive material, relational, and subjective wellbeing through their participation in DFVCs and the differences between the experiences of women and men. We argue that DFVCs are of disproportionate importance to the wellbeing of marginalized people in fishing communities, particularly women. We also examine the capacity of DFVCs to continue supporting the wellbeing of those who critically depend upon them and highlight the implications of emergent study findings for fisheries governance in Sri Lanka.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".