Forests of learning: Experiences from research on an Adaptive Collaborative Approach to community forestry in Nepal
Bibliographic record
Abstract
In recent years, awareness has grown in Nepal and globally regarding two of community forestry's most critical challenges: equity and livelihoods. Yet even as understanding of these challenges has improved, actors from the local to the national levels in Nepal continue to be confronted with the dilemma of how to address these challenges in such a diverse, complex and dynamic context. This synthesis explores an adaptive collaborative approach to governance and management as one avenue to meet these challenges. This approach integrates inclusive decision making, networking, social learning, and pro active adjustments of practice and policies based on learning. The synthesis' lessons are drawn from a six-year partnership-based research initiative in Nepal—spearheaded by the Center for International Forestry Research—which spanned the local, district and national levels. Key points of learning discussed in this book include factors, processes and arrangements that support—or limit—adaptive and collaborative capacities, such as active facilitation, ‘nested' decision making, and learning-based monitoring. The book also explores both the conceptual underpinnings of the approach as well as its effects in research sites, including in terms of benefits for the poor, women and other traditionally marginalised people. This book is intended as a resource for policy makers and civil society practitioners alike, as well as researchers and others interested in pro-equity and livelihood innovations in community forestry. Through its clear conceptual and research lesson focus, this synthesis complements and is a sister publication to the hands-on guidebook entitled Facilitating Forests of Learning.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.030 | 0.022 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".