The social science of offshore aquaculture: uncertainties, challenges and solution-oriented governance needs
Bibliographic record
Abstract
Aquaculture technology is on the move, enabling production in more open and exposed ocean environments around the world. These new systems offer solutions to environmental challenges facing conventional aquaculture, yet new technologies also create new social challenges while potentially exacerbating, or at minimum recreating, others. Offshore aquaculture research and governance are still in early stages, as is our understanding of the social repercussions and challenges associated with development. This paper provides an evaluation and reflection on offshore aquaculture from a social science perspective and is based on findings from a modified World Café group discussion method including the thoughts and experiences of social science experts. Key challenges and uncertainties including a lack of an appropriate regulatory framework, societal perceptions of offshore aquaculture, and offshore aquaculture’s contribution to society were identified. The governance implications of these challenges are discussed as well as the need for social sciences to address these challenges through transformative and transdisciplinary approaches that bridge science and society.
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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 | 0.028 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.029 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.004 | 0.004 |
| 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".