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 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 | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".