Avoiding Re‐Inventing the Wheel in a People‐Centered Approach to REDD+
Bibliographic record
Abstract
One important debate regarding Reducing Emissions from Deforestation and Forest Degradation (REDD+) in developing countries concerns the manner in which its implementation might affect local and indigenous communities. New ways to implement this mechanism without harming the interests of local communities are emerging. To inform this debate, we conducted a qualitative research synthesis to identify best practices (BPs) from people-centered approaches to conservation and rural development, developed indicators of BPs, and invited development practitioners and researchers in the field to assess how the identified BPs are being adopted by community-level REDD+ projects in Latin America. BPs included: local participation in all phases of the project; project supported by a decentralized forest governance framework; project objectives matching community livelihood priorities; project addressing community development needs and expectations; project enhancing stakeholder collaboration and consensus building; project applying an adaptive management approach; and project developing national and local capacities. Most of the BPs were part of the evaluated projects. However, limitations of some of the projects related to decentralized forest governance, matching project objectives with community livelihood priorities, and addressing community development needs. Adaptive management and free and prior informed consent have been largely overlooked. These limitations could be addressed by integrating conservation outcomes and alternative livelihoods into longer-term community development goals, testing nested forest governance approaches in which national policies support local institutions for forest management, gaining a better understanding of the factors that will make REDD+ more acceptable to local communities, and applying an adaptive management approach that allows for social learning and capacity building of relevant stakeholders. Our study provides a framework of BPs and indicators that could be used by stakeholders to improve REDD+ project design, monitoring, and evaluation, which may help reconcile national initiatives and local interests without reinventing the wheel.
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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 | 0.164 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.016 | 0.090 |
| Scholarly communication | 0.016 | 0.032 |
| Open science | 0.005 | 0.033 |
| Research integrity | 0.007 | 0.015 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".