COVID-19 impact on local agri-food system in Cambodia, Myanmar, and the Philippines: Findings from a rapid assessment
Bibliographic record
Abstract
The COVID-19 pandemic and the subsequent enforcement of mobility restrictions have created bottlenecks in the agri-food system. When the food supply chain is disrupted, economic loss occurs, putting rural households, already in poverty, into severe food insecurity. A survey was conducted to assess the impacts of restrictions brought by COVID-19 on local food systems operations of Climate-Smart Villages (CSVs) in Cambodia, Myanmar, and the Philippines. The rural and traditional food systems of agriculture-based villages continued to operate with minimal adjustments during the course of COVID-19 restrictions. Our findings showed high mean household dietary diversity scores in Chhouk CSV (6.4), Htee Pu CSV (8.2), and Himbubulo Weste CSV (7.2) despite significant perceived changes in the availability and prices of certain food groups. Complementary and diverse food production and access to informal food outlets were essential parts of the local food systems and played critical roles in supplying food commodities to the population during the pandemic.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".