Implementing REDD+ in a Conflict-Affected Country: A Case Study of the Democratic Republic of Congo
Bibliographic record
Abstract
Due to their carbon sequestration potential, tropical forests are a focal point for mitigation of climate change through Reducing Emissions from Deforestation and Forest Degradation (REDD+). The Democratic Republic of Congo (DRC) contains the largest part of the Congo Basin, the second largest rainforest in the world, and has become a main focus for REDD+ initiatives. However, DRC’s ongoing instability and conflict threatens the peace and security of local people, and outcomes of such global initiatives. Content analysis of 102 documents from four major REDD+ initiatives intervening in DRC, sought to understand how civil conflict is being integrated into the discourse on REDD+ and its implication for climate change mitigation. Results showed that discussion of how conflict and political instability might impact REDD+ outcomes was limited. Concrete approaches to address the reality of civil conflict were not evident. Governance reform was, however, an important emphasis of REDD+ in DRC. Since REDD+, peace-building and development initiatives are often funded by the same institutions, it is important to begin a dialogue as to how they can be more intentional in harmonizing approaches in conflict-affected, forest-rich countries like DRC. Finding synergies has the potential to improve overall outcomes for the global climate, the forest, and the lives of local people.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.019 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".