Unshaping Model Forests in the Global South: Trans-local Politics and Community Forestry Knowledge Circulation in Ecuador and Cameroon
Bibliographic record
Abstract
Forest communities around the world are increasingly connected to defend territorial and community rights over lands and forests. This is how the International Model Forest Network (IMFN) was created in 1995 by the Government of Canada, aiming at bringing together a diverse association of individuals and groups towards a common vision of sustainable development across extensive landscapes. In 2002, the Ibero-American Model Forest Network (RIABM) was created in Costa Rica, and in 2009 the African Model Forest Network (AMFN) emerged in Cameroon. This research examines how Model Forests’ actors build shared knowledge on community-based forest management through trans-local dynamics. It draws on a comparative analysis of the Choco Andino Model Forest, Ecuador, and the Campo Ma’an Model Forest, Cameroon. It shows the financial and project-based opportunities of being part of a Model Forest network, and the challenges related to local appropriation and visibility, political continuity, leadership and tensions with extractive activities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".