The Effect of the Nepal Community Forestry Program on Equity in Benefit Sharing
Bibliographic record
Abstract
We assessed the effectiveness of Nepalese Community Forestry Program (CFP) in increasing local perceptions of equity in benefit sharing. Our aim is to inform emerging forest policy that aims to mitigate climate change, promote biodiversity conservation, and address poverty and livelihood needs. We collected data from 1,300 households from nationally representative samples of 65 CFP communities and 65 non-CFP communities. By using a robust method of covariates matching, we demonstrate the unique and positive effect of the CFP on perception of equity in benefit sharing at national level and among poor, Dalits, indigenous and women-headed households and in the hills (except Terai). Our results suggest the need to continue the current benefit-sharing practices in CFP except in the Terai, where such practices need to be reviewed. However, caution should be taken in implementing emerging carbon-focused forestry so that it does not alter the CFP management sufficiently to conflict with equity goals and upend the generally positive effects on equity.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".