Can a “doughnut” economic framework be useful to monitor the blue economy success? A fisheries example
Bibliographic record
Abstract
In this paper, we employ a “doughnut” economic approach to comprehensively assess the state of the purse-seiners fisheries sector in the northwestern Mediterranean Sea. The analysis identifies several instances of ecological overshooting and shortages in basic social needs, indicating that the current situation is, in many respects, far from being in a secure, ecologically safe, and socially just space. It demonstrates that the necessary transition to achieve a sustainable sector is not solely a technical or financial issue; it also requires sufficient social capabilities to lead and manage the process, taking into consideration the social context in which it would occur. Our assessment indicates the need for urgent action and an overarching transition plan that includes an ecosystem-based fishery management plan, including commercial and social plans. The study showcases that this approach is useful in providing valuable information to support the transition of fisheries toward sustainability. Moreover, utilizing this non-fisheries-specific framework can facilitate the participation of fisheries expertise in broader discussions about the socioeconomic and ecological changes needed to achieve a post-growth-oriented 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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".