Evaluating the Feasibility of Sustainable Seafood Labelling Programmes in Small Island Developing States: A Pilot Study of Artisanal Fisheries in Seychelles
Bibliographic record
Abstract
The Republic of Seychelles is one of six African Small Island Developing States (SIDS) and has a marine-based economy reliant on fisheries and international tourism. Seychelles has been flagged by the United Nations as highly vulnerable to climate change. Climatic threats are compounded with population declines of key fishery species. A progressive national stance towards ocean sustainability and an emerging economy partially driven by tourists are two of several factors that make Seychelles a good candidate for a sustainable seafood labelling and consumption programme, which would provide market-based incentives for fishery harvesters, regulators, buyers and consumers to improve sustainable practices. To address the feasibility of such a programme, we conducted a pilot study, surveying 33 artisanal fishers and mapping supply chain structure to examine incentives and challenges. Questions addressed fishers’ years of experience, reliance on fishing for income, and flexibility in gear type and species targeted. Of the total number of respondents, 64% would like to see a programme implemented but only 34% thought it would be successful. Participants identified several barriers and benefits that primarily spanned socioeconomic and regulatory themes. Our pilot results indicate the sociocultural and economic impacts of sustainability programmes in Seychelles are as important as environmental considerations, a finding pertinent to anyone undertaking similar research efforts in other SIDS. We advocate for the necessity of thorough, location-based research and in-depth stakeholder consultation to elucidate economic, societal, behavioural and cultural factors that will affect the success of designing and implementing seafood labelling programmes in SIDS.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".