Responding to Environmental Issues through Adaptive Collaborative Management : From Forest Communities to Global Actors
Bibliographic record
Abstract
Focused on forest management and governance, this book examines two decades of experience with Adaptive Collaborative Management (ACM), assessing both its uses and improvements needed to address global environmental issues. The volume argues that the activation and the empowerment of local peoples are critical to addressing current environmental challenges and that this must be enhanced by linking and extending such stewardship to global and national policymakers and actors on a broader scale. This can be achieved by employing ACM’s participatory approach, characterized by conscious efforts among stakeholders to communicate, collaborate, negotiate and seek out opportunities to learn collectively about the impacts of their action. The case studies presented here reflect decades of experience working with forest communities in three Indonesian Islands and four African countries. Researchers and practitioners who participated in CIFOR’s early ACM work had the rare opportunity to return to their research sites decades later to see what has happened. These authors reflect critically on their own experience and local site conditions to glean insights that guide us in more effectively addressing climate change and other forest-related challenges. They showcase how global and regional actors will have to work more closely with smallholders, Indigenous Peoples and local communities, recognizing the key local roles in forest stewardship. This book will be of great interest to students, scholars and practitioners working in the fields of conservation, forest management, community development, natural resource management and development studies more broadly. The Open Access version of this book, available at www.taylorfrancis.com, has been made available under a Creative Commons Attribution-Non Commercial-No Derivatives 4.0 license.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.015 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| 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".