Price volatility in fish food systems: spatial arbitrage as an adaptive strategy for small-scale fish traders
Bibliographic record
Abstract
Anthropogenic stressors such as land-use change, habitat degradation, and climate change stress inland fish populations globally. Such ecological disturbances can affect actors throughout the social-ecological system by contributing to uncertainty in landings, landing prices, and coastal incomes. Most literature to date on the resilience of the fishing sector has focused on fishing (production), fisheries management, and the livelihoods of fishers, whereas little attention has been paid to the post-harvest sector and the livelihoods of fish processors, logistics providers, wholesalers, and retailers. In the empirical case of the small-scale usipa (Engraulicypris sardella) trade in Malawi, we investigated the impacts of price volatility, a form of uncertainty, on small-scale fish retailers’ livelihood outcomes. By concentrating on fish retailers in the downstream region of the value chain, we provide new insight into how small-scale fisheries actors in the broader fish food system experience and adapt to uncertainty. We find that price volatility negatively impacts net income for retailers, and that an important adaptive strategy is spatial arbitrage. However, gender dynamics and access to capital limit retailers’ ability to employ the spatial arbitrage adaptive strategy.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.003 | 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".