Adaptive co-management: collaboration, learning and multi-level governance.
Bibliographic record
Abstract
Figures, Tables, Boxes Acronyms Preface and Acknowledgments 1 Introduction: Moving beyond Co-Management / Derek Armitage, Fikret Berkes and Nancy Doubleday Part 1: Theory 2 Adaptive Co-Management and Complexity: Exploring the Many Faces of Co-Management / Fikret Berkes 3 Connecting Adaptive Co-Management, Social Learning, and Social Capital through Theory and Practice / Ryan Plummer and John FitzGibbon 4 Building Resilient Livelihoods through Adaptive Co-Management: The Role of Adaptive Capacity / Derek Armitage 5 Adaptive Co-Management for Resilient Resource Systems: Some Ingredients and the Implications of Their Absence / Anthony Charles Part 2: Case Studies 6 Challenges Facing Coastal Resource Co-Management in the Caribbean / Patrick McConney, Robin Mahon, and Robert Pomeroy 7 Adaptive Fisheries Co-Management in the Western Canadian Arctic / Burton G. Ayles, Robert Bell, and Andrea Hoyt 8 Integrating Holism and Segmentalism: Overcoming Barriers to Adaptive Co-Management between Management Agencies and Multi-Sector Bodies / Evelyn Pinkerton 9 Conditions for Successful Fisheries and Coastal Resources Co-Management: Lessons Learned in Asia, Africa, and the Wider Caribbean / Robert Pomeroy Part 3: Challenges 10 Communities of Interdependence for Adaptive Co-Management / John Kearney and Fikret Berkes 11 Adaptive Co-Management and the Gospel of Resilience / Paul Nadasdy 12 Culturing Adaptive Co-Management: Finding Keys to Resilience in Asymmetries of Power / Nancy Doubleday Part 4: Tools 13 Novel Problems Require Novel Solutions: Innovation as an Outcome of Adaptive Co-Management / Gary P. Kofinas, Susan J. Herman, and Chanda Meek 14 The Role of Vision in Framing Adaptive Co-Management Processes: Lessons from Kristianstads Vattenrike, Southern Sweden / Per Olsson 15 Using Scenario Planning to Enable an Adaptive Co-Management Process in the Northern Highlands Lake District of Wisconsin / Garry Peterson 16 Synthesis: Adapting, Innovating, Evolving / Fikret Berkes, Derek Armitage and Nancy Doubleday Glossary Contributors Index
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.048 | 0.004 |
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".