Bringing sustainable seafood back to the table: exploring chefs’ knowledge, attitudes and practices in Peru
Bibliographic record
Abstract
Abstract Conservation organizations promoting sustainable seafood have had greater success when chefs are empowered as agents of change in favour of sustainable seafood. Peru is experiencing a gastronomic revolution with seafood at its core, and Peruvian top chefs are being approached by conservation organizations to become environmental advocates. Within this context we characterize the factors that influence chefs’ behaviours regarding sustainable seafood. A total of 52 Peruvian top chefs were surveyed using the Knowledge, Attitudes and Practices Framework, complemented by a focus group with a subset of the surveyed population. Our results suggest that, regardless of their age or academic background, chefs are aware of the negative consequences that human activities have on the ocean and believe that restaurants have an obligation to become part of the solution by promoting the use of sustainable seafood. Nonetheless, three factors limit chefs’ understanding of key concepts and prevent them from fully internalizing the environmental consequences of their actions in restaurants: (1) sustainability is a new topic for them, particularly for older chefs; (2) the fish species commonly used at restaurants are poorly regulated, and (3) chefs are risk averse to actions that could result in profit loss. Additionally, the structure of the seafood supply chain further limits chefs’ capacity to act sustainably, even if they are aware of the need to change their behaviour. Recommendations are provided for future conservation campaigns advocating use of sustainable seafood, some of which have now been implemented.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".