Governance Challenges in an Eastern Indonesian Forest Landscape
Bibliographic record
Abstract
Integrated approaches to natural resource management are often undermined by fundamental governance weaknesses. We studied governance of a forest landscape in East Lombok, Indonesia. Forest Management Units (Kesatuan Pengelolaan Hutan or KPH) are an institutional mechanism used in Indonesia for coordinating the management of competing sectors in forest landscapes, balancing the interests of government, business, and civil society. Previous reviews of KPHs indicate they are not delivering their potential benefits due to an uncertain legal mandate and inadequate resources. We utilized participatory methods with a broad range of stakeholders in East Lombok to examine how KPHs might improve institutional arrangements to better meet forest landscape goals. We find that KPHs are primarily limited by insufficient integration with other actors in the landscape. Thus, strengthened engagement with other institutions, as well as civil society, is required. Although new governance arrangements that allow for institutional collaboration and community engagement are needed in the long term, there are steps that the East Lombok KPH can take now. Coordinating institutional commitments and engaging civil society to reconcile power asymmetries and build consensus can help promote sustainable outcomes. Our study concludes that improved multi-level, polycentric governance arrangements between government, NGOs, the private sector, and civil society are required to achieve sustainable landscapes in Lombok. The lessons from Lombok can inform forest landscape governance improvements throughout Indonesia and the tropics.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".