Thinking outside the box: embracing social complexity in aquaculture carrying capacity estimations
Bibliographic record
Abstract
Abstract With ever-expanding marine aquaculture, calls for sustainable development become louder. The concept of aquaculture carrying capacity (CC) emerged 30 years ago to frame development, though so far, most studies have focused on the production and ecological components, leaving aside the social perspective. Often, estimations are carried out a posteriori, once aquaculture is already in place, hence ignoring relevant voices potentially opposing the onset of aquaculture implementation. We argue that CC should be multidimensional, iterative, inclusive, and just. Hence, the evaluative scope of CC needs to be broadened by moving from industry-driven, Western-based approaches towards an inclusive vision taking into consideration historical, cultural, and socio-economic concerns of all stakeholders of a given area. To this end, we suggest guidelines to frame a safe operating space for aquaculture based on a multi-criteria, multi-stakeholder approach, while embracing the social-ecological dynamics of aquaculture settings by applying an adaptive approach and acknowledging the critical role of place-based constraints. Rather than producing a box-checking exercise, CC approaches should proactively engage with aquaculture-produced outcomes at multiple scales, embracing complexity, and uncertainty. Scoping CC with the voices of all relevant societal groups, ideally before aquaculture implementation, provides the unique opportunity to jointly develop truly sustainable aquaculture.
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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 | 0.095 | 0.159 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.005 | 0.026 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.003 | 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".