Applying Participatory Action Research Methods in Community-Based Adaptation With Smallholders in Myanmar
Bibliographic record
Abstract
The effects of climate change to agriculture being largely location specific, it is crucial that adaptation measures recognize the value of targeted, context-specific, community-based strategies and processes. This research deployed participatory action research relying on a diverse range of socio-technical methods for facilitating community-level adaptation in climate-smart villages. Smallholder farms in four unique agro-ecologies in Myanmar were targeted. Results and insights from the 3-year, participatory action research effort chronicle how the climate-smart village approach was implemented in the four targeted climate-smart villages (CSVs). The key support systems needed for effective community engagement in implementing the CSVs are discussed. Social learning helped nurture capacities of farmers to find solutions and test and improve adaptation options. Using a combination of socio-technical processes, smallholder farmers, researchers, and facilitators improved their understanding of climate change, drivers of vulnerability, and coping activities. With this knowledge and understanding, the farmers in the CSVs identified a menu of adaptation options that they would test and adopt (and scale). This “portfolio approach” to deriving adaptation options ensured that there were opportunities for men, women, and landless households to participate in the community adaptation process. This approach allowed farmers to determine what was their preferred entry point. Invariably, such approaches nurture incremental adaptation with associated incremental learning. The research suggests that land tenure regimes influence the nature of the adaptation options and their eventual uptake. In villages with high incidence of landlessness, the adaptation options were limited to homesteads, the small patch of land around the household dwelling. A more secure tenure status provided farmers with freedom to engage in diversified and long-term production systems. Poverty and wealth levels of households were other factors influencing the uptake of adaptation options, especially those aimed at diversifying production for reduced risks.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".