Overview of Small-Scale Fisheries in Latin America and the Caribbean: Challenges and Prospects
Bibliographic record
Abstract
The importance of small-scale fisheries in Latin America and the Caribbean has been widely recognized in terms of income, livelihoods, and food security for more than two million people. The highly diverse ecosystems and multiple species found within this region determine the variety of fishing techniques, gears, and target species, as discussed in this chapter. These diverse and complex characteristics pose challenges to the region’s governing systems, which may lack the technical and financial resources to cope with the numerous resulting management and governance challenges. These pressures are further exacerbated when fisheries assessment and monitoring are poorly conducted, adding uncertainty in relation to the status of the ecosystem and fish stocks. Small-scale fisheries activities thus have become vulnerable in the face of various challenges in Latin America and the Caribbean. Current efforts to enhance small-scale fisheries viability and sustainability in Latin America and the Caribbean include the adoption of innovative management approaches that focus on the entire ecosystems rather than on single species and that acknowledge the concerns of local stakeholders in decision-making through strategies such as collaboration with the government in co-management arrangements. Although many of these co-management arrangements in the region are still nascent, this chapter highlights that fishers and their organizations play a significant role in responsible resource governance through exercising ecosystem stewardship.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".