The impact of the COVID-19 pandemic on the Galapagos Islands' seafood system from consumers’ perspectives
Bibliographic record
Abstract
The COVID-19 pandemic's early stages severely impacted global fisheries, particularly areas heavily reliant on imported food and tourism like the Galapagos Islands, Ecuador. To contain the spread of the virus, a full lockdown was implemented. However, the collapse of the tourism industry precipitated the worst economic crisis in the history of this multiple-use marine protected area. This paper examines the impact of the pandemic's early stages on consumption patterns and seafood security in the Galapagos from consumers' perspective, drawing on online surveys conducted during the lockdown. Our findings revealed pre-existing seafood insecurity across the archipelago, further exacerbated by the pandemic on the least-populated island. Nevertheless, the seafood system displayed moderated resilience to the pandemic's socioeconomic disruptions. A variety of adaptive responses were adopted by Galapagos residents to cope with the lockdown. Consumers modified their seafood consumption habits, while fishers adapted their harvesting and marketing strategies. Such adaptive responses were shaped by the unique socioeconomic characteristics of each inhabited island and the ability of seafood suppliers to shift from a tourism- and export-oriented to a resident- and domestic-oriented market. This transition has created novel opportunities to foster a systemic transformation of the Galapagos seafood system to enhance its resilience against future crises caused by new pandemics, climate change, or other natural and anthropogenic drivers of change.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".