Masked, diluted and drowned out: how global seafood trade weakens signals from marine ecosystems
Bibliographic record
Abstract
Abstract Nearly 40% of seafood is traded internationally and an even bigger proportion is affected by international trade, yet scholarship on marine fisheries has focused on global trends in stocks and catches, or on dynamics of individual fisheries, with limited attention to the link between individual fisheries, global trade and distant consumers. This paper examines the usefulness of fish price as a feedback signal to consumers about the state of fisheries and marine ecosystems. We suggest that the current nature of fisheries systems and global markets prevent transmission of such price signals from source fisheries to consumers. We propose several mechanisms that combine to weaken price signals, and present one example – the N orth S ea cod – to show how these mechanisms can be tested. The lack of a reliable price feedback to consumers represents a challenge for sustainable fisheries governance. We therefore propose three complimentary approaches to address the missing feedback: (i) strengthening information flow through improved traceability and visibility of individual fishers to consumers, (ii) capitalizing on the changing seafood trade structures and (iii) bypassing and complementing market mechanisms by directly targeting citizens and political actors regarding marine environmental issues through publicity and information campaigns. These strategies each have limitations and thus need to be pursued together to address the challenge of sustainability in global marine fisheries.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".