9 steps to scale climate-smart agriculture: Lessons and experiences from the climate-smart villages in My Loi, Vietnam and Guinayangan, Philippines
Bibliographic record
Abstract
The Climate-Smart Village approach is a CCAFS agricultural research for development (AR4D) strategy for stimulating the scaling of climate-smart agriculture. CSVs are established in Southeast Asia through the CCAFS program to serve as sites for “testing, through participatory methods, technological and institutional options for generating evidence of CSA effectiveness as well as drawing out scaling lessons for policy makers from local to global levels (CCAFS, 2016). The CSVs in My Loi in Vietnam and Guinayangan in the Philippines were established following this strategy starting 2014 by the World Agroforestry (ICRAF) Vietnam and the International Institute for Rural Reconstruction, respectively. This guidebook showcases the common experiences of the IIRR and ICRAF in the Philippine and Vietnam CSVs, which are outlined in 5 major stages and broken into 9 steps.
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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 | 0.002 | 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 | 0.009 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".