The transnational policy process for REDD+ and domestic policy entrepreneurship in developing countries
Bibliographic record
Abstract
This article aims to understand the complex relationship between transnational pathways of policy influence and strategies of domestic policy entrepreneurship in the pursuit of REDD+ in developing countries. Since 2007, a complex governance arrangement exerting influence through the provision of international rules, norms, markets, knowledge, and material assistance has supported the diffusion of REDD+ policies around the world. These transnational pathways of influence have played an important role in the launch of REDD+ policy-making processes at the domestic level. Indeed, over 60 developing countries in Asia, Africa, and Latin America have initiated multi-year programmes of policy reform, research, and capacity-building that aim to lay the groundwork for the implementation of REDD+. However, there is emerging evidence that the nature of policy change associated with these REDD+ policy efforts ultimately depends on the mediating influence of domestic factors. This article offers an analytical framework that focuses on whether and how domestic policy actors can seize the opportunities provided by transnational policy pathways for REDD+ to challenge or reinforce the status quo in the governance of forests and related sectors.
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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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".