Social-ecological System Interactions in Small-scale Fisheries: Case Studies of the Large Pelagic and Shallow Reef Fisheries of Grenada and St. Lucia Under Construction
Bibliographic record
Abstract
The components of social-ecological systems, their interactions in marine fisheries and the resulting outcomes of interaction are not always obvious to many fishery stakeholders. In Grenada and St. Lucia, the small-scale fisheries for large pelagics and shallow reef fish are examples of such complex systems. A pelagic longliner or a pot fisher catching, landing and marketing fish appears to be engaged in simple activities at first glance, but there exists a network of complex relationships and human-nature interactions within these fisheries activities. If stakeholders involved in the governance of such small-scale fisheries had a better understanding of how these complex social-ecological systems function from a network perspective, then it may be possible to improve the outcomes to meet societal goals. In this paper, I provide a preliminary description of the main social-ecological components and their network interactions in the fisheries for large pelagic and shallow reef fish in Grenada and St. Lucia. This research is part of a larger study on the governance of small-scale fisheries in the eastern Caribbean.
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How this classification was reachedexpand
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 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".