Private sector perception of reducing deforestation in brazil: analysis of challenges from 2010 to 2019
Bibliographic record
Abstract
Purpose: This paper aims to shed light on the private sector's perspective on REDD+ in Brazil, and how this perspective has evolved over time. Methodology/Approach: This research is part of the Global Comparative Study on REDD+ (GCS REDD+) on policies and political processes from the Center for International Forestry Research (CIFOR). Findings: Our results indicate that national business organizations believe that REDD+ is an affordable way to mitigate climate change. However, it suggests that while this sector is seeking financial benefits from REDD+ activities, it is taking a very cautious and risk-averse approach. The private sector is not engaged and does not self-identify within the operational challenges that REDD+ policymakers are grappling with as they seek to embrace the possibilities of this mechanism. Research Limitation/Implication: To explore how these private sector actors perceive REDD+, whether such a perspective has changed from 2010 to 2019, and its implications for further REDD+ design in the national context. Originality/Value of the paper: private actors' positions on key statements about financing, benefit sharing and equity, governance, and challenges over three different time periods. A better understanding of how the private sector perceives REDD+ will contribute to national framing and more effective multi-level governance.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".