A comparative study of small-scale fishery supply chains’ vulnerability and resilience to COVID-19
Bibliographic record
Abstract
The COVID-19 pandemic and response has significantly disrupted fishery supply chains, creating shortages of essential foods and constraining livelihoods globally. Small-scale fisheries (SSFs) are responding to the pandemic in a variety of ways. Together, disruptions from and responses to COVID-19 illuminate existing vulnerabilities in the fish distribution paradigm and possible means of reducing system and actor sensitivity and exposure and increasing adaptive capacity. Integrating concepts from literature on supply chain disruptions, social-ecological systems, human wellbeing, vulnerability, and SSFs, we synthesize preliminary lessons from six case studies from Indonesia, the Philippines, Peru, Canada, and the United States. The SSF supply chains examined employ different distribution strategies and operate in different geographic, political, social, economic, and cultural contexts. Specifically, we ask (a) how resilient have different SSF supply chains been to COVID-19 impacts; (b) what do these initial outcomes indicate about the role of distribution strategies in determining the vulnerability of SSF supply chains to macroeconomic shocks; and (c) what key factors have shaped this vulnerability? Based on our findings, systemic changes that may reduce SSF vulnerability to future macroeconomic shocks include: diversification of distribution strategies, livelihoods, and products; development of local and domestic markets and distribution channels; reduced reliance on international markets; establishment of effective communication channels; and preparation for providing aid to directly assist supply chains and support consumer purchasing power.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".