COVID-19 impacts on food systems in fisheries-dependent island communities
Bibliographic record
Abstract
Policies designed to contain the COVID-19 pandemic have impacted food systems worldwide. How impacts played out in local food systems, and how these affected the lived experiences of different people is only just coming to light. We conducted a structured analysis of the impacts of COVID-19 containment policies on the food systems of small-scale fishing communities in Kenya, Papua New Guinea, and Saint Lucia, based on interviews with men and women fishers, fish traders, and community leaders. Participants reported that containment policies lead indirectly to reduced volumes of food, lower dietary diversity, increased consumption of traditional foods, and reduced access to fish for food and income. Although the initiating policy and food and nutrition security outcomes often appeared similar, we found that the underlying pathways and feedbacks causing these impacts were different based on local context. Incorporating knowledge of how context-specific factors shape food system outcomes may be key to tailoring strategies to mitigate the ongoing impacts of COVID-19 and designing timely, strategic interventions for future systemic shocks.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".