Strengthening policy action to tackle social acceptability issues in European aquaculture
Bibliographic record
Abstract
Abstract Despite the rapid development of aquaculture worldwide, production has stagnated in Europe and North America, notwithstanding the public policies that support the sector. This stagnation may stem from the insufficient integration of social dimensions into aquaculture governance, often characterized by top-down policies and technology-driven approaches. While environmental, economic and social factors significantly influence the social acceptability of aquaculture, environmental impacts, such as habitat degradation and the spread of disease, have historically dominated regulatory frameworks. Today, low social acceptability appears to be the major obstacle to the sector's growth, highlighting shortcomings in terms of stakeholder engagement, transparency and fairness in the distribution of the benefits generated by the sector. This paper reflects the collective insights from the ICES Working Group on Social and Economic Dimensions of Aquaculture, emphasizing that challenges to social acceptability of aquaculture are widespread but context-dependent and remain insufficiently addressed in public policies related to aquaculture development. This paper recommends broadening governance beyond environmental concerns to include social and economic dimensions from the outset, strengthening public participation in decision-making processes and adopting holistic, socially informed marine spatial planning. In addition, it highlights the importance of recognizing the role of informal governance mechanisms and the production of meaningful social data as essential aspects to foster community acceptance and the sustainable development of aquaculture. Adapting aquaculture policies to local contexts through inclusive and adaptive governance is therefore essential to the sustainable growth of the sector.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.084 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.002 | 0.021 |
| Research integrity | 0.010 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".