Alternative pathways to sustainable seafood
Bibliographic record
Abstract
Abstract Seafood certifications are a prominent tool being used to encourage sustainability in marine fisheries worldwide. However, questions about their efficacy remain the subject of ongoing debate. A main criticism is that they are not well suited for small‐scale fisheries or those in developing nations. This represents a dilemma because a significant share of global fishing activity occurs in these sectors. To overcome this shortcoming and others, a range of “fixes” have been implemented, including reduced payment structures, development of fisheries improvement projects, and head‐start programs that prepare fisheries for certification. These adaptations have not fully solved incompatibilities, instead creating new challenges that have necessitated additional fixes. We argue that this dynamic is emblematic of a common tendency in natural resource management where particular tools and strategies are emphasized over the conservation outcomes they seek to achieve. This can lead to the creation of “hammers” in management and conservation. We use seafood certifications as an illustrative case to highlight the importance of diverse approaches to sustainability that do not require certification. Focusing on alternative models that address sustainability problems at the local level and increase fishers’ adaptive capacity, social capital, and agency through “relational” supply chains may be a useful starting point.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.032 | 0.003 |
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".