Anticipating trade-offs and promoting synergies between small-scale fisheries and aquaculture to improve social, economic, and ecological outcomes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Blue food systems are crucial for meeting global social and environmental goals. Both small-scale marine fisheries (SSFs) and aquaculture contribute to these goals, with SSFs supporting hundreds of millions of people and aquaculture currently expanding in the marine environment. Here we examine the interactions between SSFs and aquaculture, and the possible combined benefits and trade-offs of these interactions, along three pathways: (1) resource access and rights allocation; (2) markets and supply chains; and (3) exposure to and management of risks. Analysis of 46 diverse case studies showcase positive and negative interaction outcomes, often through competition for space or in the marketplace, which are context-dependent and determined by multiple factors, as further corroborated by qualitative modeling. Results of our mixed methods approach underscore the need to anticipate and manage interactions between SSFs and aquaculture deliberately to avoid negative socio-economic and environmental outcomes, promote synergies to enhance food production and other benefits, and ensure equitable benefit distribution.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it