The context of REDD+ in Myanmar: Drivers, agents and institutions
Bibliographic record
Abstract
The Republic of the Union of Myanmar is a forest resource-rich country, but is also facing serious deforestation and forest degradation problems. Currently, Myanmar's forest still covers more than 40% of the country's land area (Aung (2001) but 70% of its population live in rural areas, and the agricultural sector is the main contributor to the country's gross domestic product (GDP) (30%) (World Bank 2014). The country faces the all-too-common dilemma of how to develop its economy while at the same time curbing environmental degradation and contributing to carbon emissions reduction. In 2013, Myanmar adopted a REDD+ program and started its preparatory phase. Myanmar established and developed its National Forest Monitoring System (NFMS) and Reference Emission Levels (RELs) for REDD+ following the guidance and modalities set out by the United Nations Framework Convention on Climate Change (UNFCCC). Implementing REDD+ requires political commitment to address direct and indirect drivers of deforestation, an adequate funding mechanism that is based on a thorough analysis of all costs and benefits, a transparent and equitable benefit-sharing mechanism, and a participatory decision-making approach in which all stakeholders can take part in REDD+. The Global Comparative Study on REDD+, together with its country partners, is compiling profiles of 14 countries to better understand the socioeconomic contexts in which REDD+ policies and processes emerge.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".