Routledge Handbook of Community Forestry
Bibliographic record
Abstract
This handbook provides a comprehensive overview and cutting-edge assessment of community forestry. Containing contributions from academics, practitioners, and professionals, the Routledge Handbook of Community Forestry presents a truly global overview with case studies drawn from across Africa, Asia, Europe, and the Americas. The Handbook begins with an overview of the chapters and a discussion of the concept of community forestry and the key issues. Topics as wide-ranging as Indigenous forestry, conservation and ecosystem management, relationships with industrial forestry, trade and supply systems, land tenure and land grabbing, and climate change are addressed. The Handbook also focuses on governance, looking at the range of approaches employed, including multi-level governance and rights-based approaches, and the principal actors involved from local communities and Indigenous Peoples to governments and national and international non-governmental organisations. The Handbook reveals the importance of the historical context to community forestry and the effects of power and politics. Importantly, the Handbook not only focuses on successful examples of community forestry, but also addresses failures in order to highlight the key challenges we are still facing and potential solutions. The Routledge Handbook of Community Forestry is essential reading for academics, professionals, and practitioners interested in forestry, natural resource management, conservation, and sustainable development.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.199 | 0.103 |
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".