Impacts of the COVID-19 pandemic response on aquaculture farmers in five countries in the Mekong Region
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
Public health measures aimed at reducing the spread of COVID-19 can have significant, unintended impacts on livelihoods. In this paper, we assess the impacts of responses to the COVID-19 pandemic on aquaculture farmers in five countries in the Mekong Region. A total of 1,019 farmers were surveyed (June–August 2020). The COVID-19 pandemic reduced farmer mobility, disrupted input and produce logistics, and reduced consumer demand, which in turn, reduced net income relative to expectations and increased the likelihood of making a net loss in the first half of 2020. Large aquaculture farms were more likely to experience adverse impacts from higher input prices and lower fish market prices than small farms. Intensive and commercial farms were more likely to be affected by supplier and buyer logistic disruptions. Coping responses included adjustments to stocking practices, reducing labor inputs, finding new markets, drawing on savings, and borrowing money. Large farms were more likely to seek new markets and borrow money. Easier loan conditions and direct cash handouts by governments helped in some locations and were desired in others. Significant differences among countries in impacts and responses reflect market and trade dependencies, as well as government capacity and willingness to support the aquaculture industry.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it