The Place of Community Forest Management in the REDD+ Landscape
Bibliographic record
Abstract
Community forest management (CFM) is identified by many actors as a core strategy for reducing emissions from deforestation and forest degradation in developing countries (REDD+). Others however see REDD+ as a danger to CFM. In response to these contrasting views, we carried out a systematic review of CFM case studies to look at CFM’s potential role in achieving forest carbon benefits and social co-benefits for forest communities. We evaluated the potential impacts of REDD+ on CFM. Our review showed that there is strong evidence of CFM’s role in reducing degradation and stabilizing forested landscapes; however, the review also showed less evidence about the role of CFM in reducing deforestation. For social benefits, we found that CFM contributes to livelihoods, but its effect on poverty reduction may be limited. Also, CFM may not deal adequately with the distribution of benefits within communities or user groups. These insights are important for CFM-based REDD+ intervention; measures should be adopted to overcome these gaps. Innovative incentive structures to existing CFM are discussed. The recognition of rights for forest communities is one first step identified in promoting CFM. We call for sound empirical impact evaluations that analyze CFM and CFM-based REDD+ interventions by looking at both biophysical and social outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".