Developing Local Sustainable Seafood Markets: A Thai Example
Bibliographic record
Abstract
Increasingawareness of degradation in ocean ecologies and fisheries has made seafood a leading edge in the green marketing movement, with most major buyers in the global North committing to buying seafood that has been certified as sustainable. But what about the significant and growing Asian markets, where seafood has become a healthy and prestigious food choice among wealthier consumers? Is it possible to develop a market for sustainably produced seafood among Asian consumers motivated by civil and ecological concerns? To address these questions our research traces how a Thai Fisherfolk Shop, located 4 hours to the south of Bangkok, has worked to develop an alternative market for seafood caught by local, small‐scale fishers. Although we find that there is a mismatch between the volume of aquatic species that fishers catch, the ability of the Shop to process, store, and sell seafood, and consumer demand, our analysis suggests that it is possible to create a market for small‐scale, sustainably sourced seafood in Thailand.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".