Facilitating forests of learning: Enabling an adaptive collaborative approach in community forest user groups: a guidebook
Bibliographic record
Abstract
In this guidebook, we share suggestions for how a team of facilitators and a community forest user group (CFUG) can catalyse and maintain an approach to governance and management that draws on and strengthens the CFUG's own adaptive and collaborative capacities. This approach fits within the Community Forestry framework and supports CFUGs in addressing two fundamental challenges: equity and the generation of livelihood benefits. In our experience, active and thoughtful facilitation of this approach can help CFUGs make their governance more inclusive, address tensions within the group, create more active groups with greater shared ownership of the community forest, and spark more livelihood generation activities, including for the poor. The transition to such an approach is not an easy or straight path: it involves changing relations and perspectives. Groups and their facilitators may use the suggestions in this book to help guide them as they travel on their journey, but the choices and steps are ultimately their own. Similarly, the specific outcomes of the change will be unique in each context. But this is also a strength: just as every CFUG is unique and everchanging, so its aspirations and its optimal strategies of governance and management will also be unique and ever-changing. We sincerely hope that this guidebook will prove useful to you in your own community forestry journey.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.044 | 0.024 |
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".