REDD+ policy making in Nepal: toward state-centric, polycentric, or market-oriented governance?
Bibliographic record
Abstract
Over the past 40 years, Nepal has become renowned for its community-based forestry policies, initiatives, and institutions, characterized by local autonomy in decisions about forest management and use and a gradual shift toward more inclusive national policy processes.In recent years, the government, international nongovernmental organizations (NGOs), donors, and some civil society organizations have instigated policy and piloting initiatives for an international climate change mitigation scheme known as "reducing emissions from deforestation and forest degradation and enhancement of forest carbon stocks in developing countries" (REDD+).Although many people see REDD+ as a means of bolstering forest conservation efforts and enhancing rural livelihoods, its broader implications for decentralized forest governance in Nepal and elsewhere remain uncertain and contested.Using policy network analysis and theories of polycentric and network governance, I examined influence, inclusiveness, and deliberation among actors involved in REDD+ policy making in Nepal.Data were collected between June and December 2011 through a survey of 34 organizations from government, civil society, educational and research institutions, international NGOs and donors, and the private sector.I investigated whether policy processes and the configurations of actors involved reflect state-centric, market-oriented, or polycentric governance, and I discuss the implications for decentralized forest governance in general and for the implementation of REDD+ in particular.Results indicate that REDD+ policy making is dominated by a "development triangle", a tripartite coalition of key government actors, external organizations (international NGOs and donors), and select civil society organizations.As a result, the views and interests of other important stakeholders have been marginalized, threatening recentralized forest governance and hampering the effective implementation of REDD+ in Nepal.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".