Innovation Systems in Forest Resources Management: Lessons Learned From Community Forestry Programme of Nepal
Bibliographic record
Abstract
"There have been various attempts to engage states, markets and communities in managing natural resources to achieve both conservation and poverty reduction. In Nepal, a participatory approach to forest management popularly known as 'community forestry' (CF) has proven effective in conserving forests and meeting the livelihood needs of forest-dependent communities. Since 1978, CF has evolved at both the local institutional and national policy levels. However, uneven socioeconomic relations, power dynamics, cultural contexts and other factors pose a challenge for sustainable livelihoods. Moving away from traditional research and extension services, a new emphasis on innovation systems approach has emerged. This approach demands greater attention to interactions among actors in knowledge creation, dissemination and knowledge into use. This research draws on the decade-long experience of Forest Action in adaptive, collaborative processes and management approaches, self-monitoring, and participatory action and learning with 60 community forest users groups (CFUGs) in three districts of Nepal. Preliminary results reveal effective forest management and governance innovations, adoption of planning and self-monitoring in enterprise development, and marketing of forest products and services to user groups. Furthermore, CF service providers and collaborators employ more adaptive and collaborative approaches and are more responsive to the demands and concerns of forest users and other socially marginalized groups."
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".