Training on Establishing Climate-Smart Villages (CSVs) in Myanmar to Improve Food Security and Resilience in Agriculture
Bibliographic record
Abstract
This training on establish Climate-Smart Villages (CSVs) in Myanmar was a collaborative effort of the Food Security Working Group (FSWG) and the Myanmar Program of the International Institute of Rural Reconstruction. This was supported in part by donors of the FSWG and the International Development Research Center-Canada through the 3-year action research project of IIRR-Myanmar in 4 CSVs. The overall goal of this training was to increase the understanding of the concepts, processes and tools in implementing of CSVs as an approach to build climate resilience among small-holder farmers, achieve nutrition security and gender equality in Myanmar. It was aimed for local NGOs and members of the Food Security Working Group (FSWG), Myanmar’s largest alliance of development organizations advocating for food security and sustainable livelihoods in Myanmar. This training was part of IIRR-Myanmar’s out-scaling pathway by engaging and building capacities of local civil societies to replicate the CSV approach as platforms to promote climate smart agriculture in Myanmar.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.003 |
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".