Social ties explain catch portfolios of small‐scale fishers in the Caribbean
Bibliographic record
Abstract
Abstract Small‐scale fisheries often involve weak management regimes with limited top‐down enforcement of rules and minimal support from legal institutions, making them useful model systems for investigating the role of social influence in determining economic and environmental outcomes. In such regimes, interpersonal relationships are expected to have a strong effect on a fisher's catch portfolio, the set of fish species targeted by an individual fisher. Here, we test three competing hypotheses about social influence using belief propagation network models and show that a peer‐to‐peer information‐sharing social network is key to explaining catch portfolios at a small‐scale fishery in Jamaica. We find that experience dictates the direction of influence among fishers in the social network, with older fishers and information brokers having distinct roles in shaping catch patterns for large‐ and small‐sized fish species, respectively. These findings highlight concrete opportunities for harnessing social networks in natural resource management. Our new approach to modelling social influence is applicable to many social–ecological systems with minimal legal and institutional support or those that rely heavily on bottom‐up participatory processes.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".