The role of corporate social responsibility in creating a Seussian world of seafood sustainability
Bibliographic record
Abstract
Abstract Approaches to counter the overfishing and aquaculture production crisis include those imposed by public governing bodies, as well as those implemented by businesses and non‐governmental organizations ( NGO s). In the case of the latter, private actors govern fisheries consumption and production through corporate social responsibility ( CSR ). In this contribution, we focus on three key tools that businesses are increasingly turning towards in an effort to meet the one particular CSR goal of sustainable seafood sourcing. In this context, the key tools of certifications, fisheries improvement projects ( FIP s) and traceability are reviewed, and their potential as well as limits in contributing to continual improvement in pursuit of global seafood sustainability are analyzed. We argue that seafood CSR has created its own whimsical and fantastical world, a Seussian world, in which company image has become more important than sustainability performance. We posit four important barriers that must be overcome to bring seafood CSR back to reality. Specifically, we suggest moving away from the business case for CSR , reducing accessibility barriers for small‐scale and developing world fisheries, reconciling different labels and sustainability concepts, and better recognizing the imperative role of the state in governing fisheries and seafood.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".