Assessing Forest Governance in the Countries of the Greater Mekong Subregion
Bibliographic record
Abstract
The forest landscapes of the Greater Mekong Subregion (GMS) are changing dramatically, with a multitude of impacts from local to global levels. These changes invariably have their foundations in forest governance. The aim of this paper is to assess perceptions of key stakeholders regarding the state of forest governance in the countries of the GMS. The work is based on a quantitative and qualitative analysis of the perceptions of forest governance in the five GMS countries, involving 762 representatives from government, civil society, news media, and rural communities. The work identified many challenges to good forest governance in the countries in the region, as well as noting reasons for optimism. Generally speaking, there was a feeling that the policies, legislation, and institutional frameworks were supportive, but there are numerous challenges in terms of implementation, enforcement, and compliance. The work also presents a program of activities recommended by the research participants to address governance challenges and opportunities in the GMS countries. These include the development of a forest governance monitoring system, and initiatives that support informed decision-making by forest product consumers in the region as well as the implementation of a capacity development program for non-state actors (e.g., civil society, news media) to ensure they are more able to support the diverse, and often demanding, forest governance initiatives.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".