The influence of the Sustainable Seafood Movement in the US and UK capture fisheries supply chain and fisheries governance
Bibliographic record
Abstract
Over the last decade, a diverse coalition of actors has come together to develop and promote sustainability initiatives ranging from seafood eco-labels, seafood guides, traceability schemes, and sourcing policies in Western seafood supply chains. Based on a literature review, we trace the development of the Sustainable Seafood Movement, which has been working to reform sustainability practices in the seafood supply chain. Focusing on the US and the UK capture fisheries, we explore the roles of key actors and analyze the dynamics within and between actor groups through a conceptual model derived from semi-structured interviews. We argue that the Sustainable Seafood Movement is different from previous social movements in that, in addition to actors advocating for government reform, it has motivated supply chain actors to participate in non-state market driven governance regime. The movement and its actors have leveraged their legitimacy and authority garnered within the supply chain to increase their legitimacy and authority in public governance processes. As the movement continues to evolve, it will need to address several emerging issues to maintain its position of legitimacy and authority in both the supply chain and public governance processes.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 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".