Nepal’s REDD+ Readiness Preparation and Multi-Stakeholder Consultation Challenges
Bibliographic record
Abstract
Nepal is currently undergoing a Reducing Emission from Deforestation and Forest Degradation, sustainable management of forest, and conservation and enhancement of carbon (REDD+) readiness process. The Government of Nepal has announced a high level political commitment, willingness and preparedness to attract diverse interests in policy deliberation for its REDD+ process. This paper examines Nepal’s REDD+ policy deliberation process from a political ecology perspective, focusing on expressions of discursive power and representation within Nepal’s ongoing multi-stakeholder REDD+ preparation. The analysis is based on interviews, policy document reviews and observations of public consultations to solicit comments for REDD+ strategy during the year 2013-2014. The analysis found that Nepal’s institutional REDD+ planning structure is highly dominated by techno-bureaucratic top-down practices representing government interests and international donors’ requirements, while sub-national and non-governmental stakeholders often find themselves to be merely used to legitimize the policy process rather than to actively shape it. A considerable share of policy preparations is left to the outsourced experts, and the multi-stakeholder consultation meetings have proven to be ineffective to bring the weak actors’ perspectives that actually participate in those meetings. Both the ‘geographical space’ and ‘political space’ offered in the consultations are not favourable for the local actors, but are controlled by the dominant actors. Overall, our analysis highlights important challenges and an urgent need to improve design and practice of the consultation process in order to ensure a sound multi-stakeholder process so as to meet the demands of the local forest realities as well as those of the international REDD+ requirements.Journal of Forest and Livelihood 13 (1) May, 2015, Page :30-43
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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 | 0.035 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.009 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".