A comparative study of small-scale fishery supply chains’ vulnerability and resilience to COVID-19
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.004 |
| 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 it