Multilevel Governance for Forests and Climate Change: Learning from Southern Mexico
Bibliographic record
Abstract
Reducing emissions from deforestation and forest degradation (REDD+) involves global and national policy measures as well as effective action at the landscape scale across productive sectors. Multilevel governance (MLG) characterizes policy processes and regimes of cross-scale and cross-sector participation by multiple public and private actors for improved legitimacy and effectiveness of policy. We examine multilevel, multi-actor engagement in REDD+ planning in Quintana Roo, Mexico, to find out how local perspectives align with the national policy approach to REDD+ as an integrating element of holistic rural development at territorial scale, and how current practices support procedurally legitimate MLG required to implement it. We find that there is wide conceptual agreement on the proposed approach by a variety of involved actors, in rejection of the business-as-usual sectoral interventions. Its implementation, however, is challenged by gaps in horizontal and vertical integration due to strong sectoral identities and hierarchies, and de facto centralization of power at the federal level. Continued participation of multiple government and civil society actors to contribute to social learning for locally appropriate REDD+ actions is likely to require a more balanced distribution of resources and influence across levels. Meaningfully engaging and ensuring the representation of local community interests in the process remains a critical challenge.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".