Rapidly increasing eco‐certification coverage transforming management of world’s tuna fisheries
Bibliographic record
Abstract
Abstract Tuna support some of the world's largest, most valuable and spatially extensive fisheries, but effective management has been challenging due to their transboundary movements and the need for multilateral decision‐making. To address public concerns of over‐exploitation, fishing companies have sought to differentiate themselves through involvement in “sustainable seafood” eco‐certification programmes. Here, we show that the volume associated with such initiatives for tuna increased 237‐fold between 2007 and 2019. Today, 2.31 million tonnes (47%) of the global tuna catch comes from fisheries holding or seeking Marine Stewardship Council eco‐certification. This is due to a 57‐fold increase in the number of fisheries engaged in the eco‐certification process. Crucially, this growth is also correlated with a concurrent 14‐fold increase in the adoption of harvest strategies by Regional Fisheries Management Organizations (RFMOs). Semi‐structured interviews with a broad range of RFMO stakeholders corroborate that the rapid uptake of harvest strategies is largely attributable to pressure from fishing companies needing to meet eco‐certification requirements; over 90% of respondents had directly observed or speculated on this type of advocacy. These results suggest the tuna fishing industry and associated seafood supply chain actors are now playing an unprecedented role in shaping the international governance of these species.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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".