Corporate Social Responsibility (CSR) Practices of the Largest Seafood Suppliers in the Wild Capture Fisheries Sector: From Vision to Action
Bibliographic record
Abstract
Corporate social responsibility (CSR) in the seafood industry is on the rise. Because of increasing public awareness and non-governmental organization (NGO) campaigns, seafood buyers have made various commitments to improve the sustainability of their wild seafood sourcing. As part of this effort, seafood suppliers have developed their own CSR programs in order to meet buyers’ sourcing requirements. However, the CSR of these companies, many of which are mid-supply chain or vertically integrated, remain largely invisible and unstudied. In order to better understand how mid-chain seafood suppliers engage in sustainability efforts, we reviewed the CSR practices of the 25 largest seafood companies globally (by revenue) that deal with wild seafood products. Based on literature, existing frameworks, and initial data analysis, we developed a structured framework to identify and categorize practices based on the issues addressed and the approach used. We found companies implement CSR to address four key areas, and through various activities that fit into five categories: Power; Practices; Partnerships; Public policy; and Philanthropy. One of the biggest gaps identified in this study is the lack of accountability mechanisms, as well as robust and consistent accounting of impacts. Indeed, many companies express commitments without clear goals and structures in place to ensure implementation. Therefore, improvements in seafood company performance on social and environmental aspects may not only require creating a better business case for CSR, but also require ensuring that companies have the necessary processes and structures in place through public oversights and regulations.
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".