Diverse fisher-trader relations shape responses of small-scale fisheries to global change
Bibliographic record
Abstract
Small-scale fisheries are likely to experience a higher frequency and magnitude of environmental and socioeconomic change because of increasing climate shocks and pressures that result from them, as well as because of the influence of global market dynamics. Fisheries’ responses to the impacts of global change are often influenced by relations between fishers and traders. Such relations constitute a link between markets, fishers, and the marine ecosystems. However, the ways that fisher-trader relations respond to global change, influencing the adaptive capacities of small-scale fisheries are poorly understood. Addressing this gap in this paper, we explore how fisher-trader relations, embedded within other social, ecological, and social-ecological relations, mediate change, such as disasters, new policies, or market demand. We do this by mapping the interactions that shape the mediating role of the fisher-trader relations in five case studies of small-scale fisheries. Synthesizing among the case studies we develop a typology of combinations of relations, their roles, and characteristics that influence the capacity of small-scale fisheries to respond to abrupt, slow, and cyclical change, resulting in absorbing or reinforcing its effects. Particularly we show how fisher-trader relations can generate the capacity to maintain livelihoods and form new relations when exposed to disruptive change and the capacity to increase supply in response to new market opportunities. The findings highlight the importance of studying responses to change in small-scale fisheries through the lens of relations and combinations of relations rather than individual behaviors. Future research on this topic could explore how the identified patterns of relations, including fisher-trader relations, may mediate change in other socio-cultural and social-ecological contexts, and when exposed to different types of disturbances.
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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 | 0.004 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".