Early REDD+ Implementation: The Journey of an Indigenous Community in Eastern Panama
Bibliographic record
Abstract
Reducing Emissions from Deforestation and Forest Degradation (REDD+) offers developing countries an opportunity to engage in global climate change mitigation through the sale of carbon credits for reforestation, avoided deforestation and forest conservation projects. Funding for REDD+ projects has increased in recent years and REDD+ projects have proliferated, but relatively few studies have, as yet, examined their implementation. Here, we present a synthesis of the challenges and lessons learned while implementing a REDD+ project in an Emberá community in Panama. Our case study, documented in four cycles of collaborative action research over 11 years, examines how local communities sought to reduce emissions from deforestation and benefit from carbon offset trading while improving local livelihoods. Through semi-structured interviews and participatory methods, we found that success with REDD+ hinges on broader issues than those widely discussed in the literature and in policy circles. Though economic incentives for participants and the equitable distribution of benefits remain important to project participants, our study finds that, in adapting REDD+ strategies to best suit community needs, the role of a support system for implementation (“bridging institutions”) and REDD+’s potential as a conflict resolution mechanism for tenure issues deserve more attention as key factors that contribute to meaningful participation in REDD+.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.025 | 0.008 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".