The contribution of community forestry to climate change adaptive capacity in tropical dry forests: lessons from Myanmar
Bibliographic record
Abstract
While community forestry (CF) is increasingly promoted as a climate change adaptation strategy, few analyses have examined the contribution of CF to adaptive capacity. We used a sustainable livelihood approach and Ostrom's design principles for managing commons, to assess how CF confers climate change adaptive capacity in two communities in Myanmar. Our findings indicate that CF provides tangible contributions to human and social capital, by increasing landless and female forest users' knowledge of forest management. However, CF has yet to enhance the physical, financial, and natural capital within these communities. The major challenges preventing the enhancement of socioeconomic benefits include limited community participation and weak institutional systems for monitoring and conflict resolution. We argue that CF increases community engagement in natural resource management, but in the absence of effective monitoring and decision-making, socioeconomic benefits to communities from CF programs may be limited. Our results elucidate important factors limiting the uptake and progress of CF as a viable climate change adaptation strategy in Southeast Asia, and indicate that comparative research is needed to better understand the factors that influence CF efficacy in forest- and natural resource-dependent communities globally.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".