Community-centered approaches to aquaculture in small-scale fisheries
Bibliographic record
Abstract
Aquaculture is the fastest-growing seafood production system globally, offering economic and social opportunities for small-scale fishing communities. Yet, it is often introduced through top-down, technocratic approaches that ignore the social-ecological realities of these communities. Drawing on four case studies from Mexico and Honduras, this study uses a multistage, participatory mixed-methods approach to examine the integration of aquaculture into existing fisheries. We focus on governance, social organization, economic assets, and cultural traditions, key yet understudied dimensions of implementation. Findings reveal that projects overlooking these interactions risk deepening inequities, displacing traditional livelihoods, and weakening community cohesion. Success depends on early economic support, recognition of local traditions and social structures, and the creation of context-specific governance systems. Centering community needs and experiences can help design equitable, place-based aquaculture initiatives that strengthen, rather than disrupt, small-scale fishing livelihoods.
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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 | 0.015 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.013 | 0.019 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".