Diverse Value Chains, Pricing Strategies, and Price Information Sources of Selected Dried Fish Varieties in Sri Lanka
Bibliographic record
Abstract
The dried fish industry is a diverse and dynamic sub-sector nurturing upon the fisheries sector yet mostly invisible and poorly documented. Hence, this study aims to identify the diverse value chains, roles, and main functions, different pricing strategies, and price information sources of sprats, skipjack tuna, and smoothbelly sardinella, which are the most consumed dried fish varieties in Sri Lanka irrespective of the income levels. Fifty dried fish processors were selected through a simple random sampling technique. Dried fish wholesalers (n=20) retailers (n=20), input suppliers (n=5), and dried fish consumers (n=40) were selected through convenient and snowball sampling techniques. The study was conducted in the Matara, Puttalam, and Jaffna districts representing major dried fish-producing towns from three provinces. Descriptive and inferential data analysis methods were applied such as Friedman test. Value chain (I) is the major chain for skipjack tuna and sprats indicating 37% and 30% respectively. Value chain (VI) is the major chain for smoothbelly sardinella indicating 38% out of total value chain. Cost plus (P<0.05, x̄>3.55) and competition (P<0.00, x̄>4.12) based pricing were the main pricing strategies adopted by each value chain actor. Price information is shared among each other through personal contacts intra region (P<0.05, x̄> 4.85) and inter region (P<0.05, x̄> 4.85). Therefore, cost plus and competitive based pricing strategies should be structured nationally.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".