Can fisheries be “regenerative”? Adapting agroecological concepts for fisheries and the blue economy
Bibliographic record
Abstract
Regenerative design, in which agricultural practices are organized to work with nutrient cycles and successional processes, is increasingly being explored in food systems research and practice. In this commentary, I explore whether regenerative design concepts can be adapted to marine contexts, given increased global interest in the potential of marine ecosystems to support sustainable development, i.e., the blue economy. There are numerous fundamental ecological differences between terrestrial and marine ecologies that make it difficult to directly translate regenerative farming's focus on managing the nutrient cycle. However, building on a framework for regenerative food systems that focuses on how production activities are organized rather than the specific practices and technologies in use, I find multiple useful parallels to familiar patterns in the fisheries literature, specifically, fishing down the food web, poverty traps, and portfolio-based fishing. I conclude with a discussion of directions for research on regenerative fisheries and concerns regarding the potential for greenwashing under the banner of a regenerative blue economy.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.018 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".