Optimism, hopes and fears: local perceptions of REDD+ in Nepalese community forests
Bibliographic record
Abstract
SUMMARY This paper examines local views and experiences of Reducing Emissions from Deforestation and Forest Degradation (REDD+) in Nepal, using a mixed-method political ecology approach in three community forest user groups across Nepal's diverse forest ecoregions with varying levels of REDD+ experience. The study finds positive expectations of REDD+ to varying degrees, paired with key concerns arising throughout REDD+ implementation. In particular, forest products needed for livelihood practices cannot be fully replaced by monetary benefits of REDD+ for forest harvesting restrictions. Further, increased elite capture, corruption, and power shift away from the community through the alliance of local elites with external actors in response to increased upward accountability for carbon increments. The findings urge that REDD+ should scrutinize and mitigate local adverse effects on existing community governance, and its goals need to be carefully reconciled with the local non-monetary livelihood needs.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".