Impacts of the COVID-19 pandemic response on aquaculture farmers in five countries in the Mekong Region
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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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".