Deforestation, Territorial Conflicts, and Pluralism in the Forests of Eastern Panama: A Place for Reducing Emissions from Deforestation and Forest Degradation?
Bibliographic record
Abstract
Deforestation is a primary contributor to global climate change. When the forest is felled and the vegetation is burnt or decomposes, carbon dioxide, a greenhouse gas, is released into the atmosphere. An approach designed to stem climate change is Reducing Emissions from Deforestation and Forest Degradation (REDD+), a global financial mechanism that requires intricate governance requirements to be met—a significant challenge in the developing areas. In Panama, the government is responsible for designing and implementing a national REDD+ strategy with support from multilateral organizations. This case study is built through the experience of a public hearing on the potential implementation of REDD+ in the highly contested Upper Bayano Watershed in eastern Panama. The Upper Bayano Watershed is composed of vast and diverse forest ecosystems. It forms a part of the Choco-Darien ecoregion, a global biodiversity hotspot, and is home to two Indigenous groups (Kuna and Embera) and populations of migrant farmers (colonos), all with different histories, traditions, and worldviews concerning forests and land management, often resulting in territorial conflicts. A major socioecological issue facing the region is deforestation, which is driving biodiversity loss and landscape change and threatening traditional livelihoods and cultures. The public hearing stimulates difficult discussions about access to land, tenure security, biodiversity conservation, poverty reduction, identity, power, trade-offs, and social justice. The case is designed to confront participants with the challenges of implementing ambitious, international, and often-prescriptive natural resource policies at local levels.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.012 | 0.009 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".