Attitudes and misconceptions towards sharks and shark meat consumption along the Peruvian coast
Bibliographic record
Abstract
Misconceptions, lack of knowledge, and negative attitudes towards sharks act as barriers preventing actions required to tackle threats to shark populations, limiting the success of global shark conservation initiatives. Peru, a major player for the international trade of shark products, recently approved the 'National Action Plan for the Conservation and Management of Sharks, Rays and Chimaeras' (PAN-Tib); a guiding document for conservation initiatives aimed at these fishes. Within PAN-Tib, the assessment of Peruvians' current knowledge and attitudes towards sharks is listed as a research priority. Between June and October 2016, 2004 Peruvians were surveyed along the coast to characterize their (i) shark meat consumption patterns, and (ii) knowledge and attitudes towards sharks. Results suggest that shark meat consumption is extended, but not necessarily frequent, and higher in the northern regions of the country. However, 77.5% of shark meat consumers were unaware that they had eaten sharks. Although 57.6% of the participants recognized that sharks are present in Peruvian waters, only 19.4% of the surveyed population was capable of naming at least one local shark species. Moreover, Peruvians have very negative attitudes towards sharks. They fear them and view them as man-eaters, despite this, no shark attacks have ever been reported in the country. These results highlight the need to: (i) encourage sustainable shark meat consumption, and (ii) promote communication campaigns aimed at increasing knowledge about sharks, and their importance as a source of employment and food for coastal communities, as for the national economy.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".