Evolving Perspectives of Stewardship in the Seafood Industry
Bibliographic record
Abstract
Humanity has never benefited more from the ocean as a source of food, livelihoods, and well-being, yet on a global scale this has been accompanied by trajectories of degradation and persistent inequity. Awareness of this has spurred policymakers to develop an expanding network of ocean governance instruments, catalyzed civil society pressure on the public and private sector, and motivated engagement by the general public as consumers and constituents. Among local communities, diverse examples of stewardship have rested on the foundation of care, knowledge and agency. But does an analog for stewardship exist in the context of globally active multinational corporations? Here, we consider the seafood industry and its efforts to navigate this new reality through private governance. We examine paradigmatic events in the history of the sustainable seafood movement, from seafood boycotts in the 1970s through to the emergence of certification measures, benchmarks, and diverse voluntary environmental programs. We note four dimensions of stewardship in which efforts by actors within the seafood industry have aligned with theoretical concepts of stewardship, which we describe as (1) moving beyond compliance, (2) taking a systems perspective, (3) living with uncertainty, and (4) understanding humans as embedded elements of the biosphere. In conclusion, we identify emerging stewardship challenges for the seafood industry and suggest the urgent need to embrace a broader notion of ocean stewardship that extends beyond seafood.
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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 | 0.016 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.063 |
| Scholarly communication | 0.021 | 0.023 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.009 | 0.011 |
| 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".