Fiscal policy and its implication for community forestry in Nepal
Bibliographic record
Abstract
SUMMARY This paper reviews the existing forest policies in Nepal mainly focussing on the fiscal policy of Community Forestry (CF), and discusses some of the existing and potential issues on the implication of fiscal policy. The review shows that Nepalese CF has some unclear and inconsistent legal provisions related to fiscal policy. The semi-structured interviews and focus group discussions with individuals from governmental and non-governmental organizations, Community Forest User Groups (CFUGs) from Parbat, Baglung and Dolakha districts of Nepal, and forest product traders from these districts demonstrate that there are a number of issues and challenges related to fiscal policy that have affected the promotion of sustainable and market-oriented management of forest resources. These issues and challenges have a direct impact on CFUGs to optimally benefit from their forest resources. As several stakeholders (including non-governmental organizations) are involved in implementation of CF program and yet do not have a direct influence on policy formulation, good coordination among government units, CFUGs and non-governmental organizations and their active involvement in the policy-making process could help to address the practical issues and challenges related to CF policy. This in turn would help to develop the policy consistent and unambiguous.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".