The mitigating role of climate smart villages to the impacts of COVID-19 pandemic in the Myanmar rural communities
Bibliographic record
Abstract
Climate smart village approach is identified as an important strategy laid out in the Myanmar Climate Smart Agriculture Strategy (MCSAS, 2016) Four climate smart villages were established in 2017 to facilitate participatory action research to develop the CSV approach as well as to generate evidence of outcomes. The CSV approach is based on the principle of community-directed research process where community-members collaborate with an external researcher to investigate community challenges and their solutions. Like other countries in 2020, the height of the COVID-19 pandemic, Myanmar implemented wide-scale national and local restrictions on mobility that impacted trade and business resulting to an economic slowdown. Rural communities dominated by smallholder agriculture in Myanmar are not spared from the negative impacts of these restrictions. This paper seeks to assess the impacts of the COVID-19 pandemic to the 4 climate smart villages in Myanmar by analyzing household survey data (N = 527) collected in 2020 during the height of economic disruptions and comparing these data to the household survey conducted during the pre-pandemic period of 2018. Our analysis indicated that overall, the effect of the pandemic to agriculture production in 2020 production season in the 4 CSVs has been minimal as evidenced by the continued agriculture production at the same levels as the pre-pandemic conditions in 2018. The effects to household food security and diet diversity has been varied. Sakta village in Chin state in the highlands have demonstrated that diversified production systems enable them to achieve food security in the pandemic year of 2020.
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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 | 0.020 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".