Sustainability outcomes of aquaculture eco‐certification: Challenges and opportunities
Bibliographic record
Abstract
Abstract Both the aquaculture industry and eco‐certification of aquaculture have grown significantly over the past 20 years, but the extent to which aquaculture eco‐certification is effective in creating positive environmental and societal outcomes is uncertain. Therefore, a scoping review of research on the effectiveness of eco‐certification in improving aquaculture sustainability outcomes, based on systematic search and inclusion criteria, was conducted. Challenges in producing sustainability outcomes through eco‐certification were identified, including (1) choosing which components of sustainability to reflect in eco‐certification criteria, (2) the risk of limiting improvements in sustainability by labelling a product ‘sustainable’, (3) accounting for different spatial scales of aquaculture effects, and (4) designing and applying sustainability criteria that work across different local environments. Potential approaches to these challenges include applying an ecosystem services framework to the identification of issues that could be addressed by eco‐certification criteria, supporting continuous improvement of industry best practices, incorporating criteria related to the far‐field effects of aquaculture, and recognising and accounting for the impact of local conditions on farming and eco‐certification. Although alternate governance approaches may be better suited to ensuring improved sustainability outcomes, potential improvements to eco‐certification criteria and processes are presented as opportunities to match the effectiveness of eco‐certification in creating positive sustainability outcomes to its success in creating a market for eco‐certified farmed seafood. However, some of these improvements may require the addition of criteria or complexity within the eco‐certification process, and their impact on market outcomes, particularly the participation of producers, should be considered.
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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 | 0.079 | 0.150 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".