Potential for integrated landscape approaches: A review of Indonesia’s national environment and development policies
Bibliographic record
Abstract
Key messages This brief explores Indonesia’s national environment and development policy climate and whether it is conducive to operationalizing an integrated landscape approach (ILA). The findings presented here complement a parallel infobrief by Maryani et al. (2021). We find policies and development plans such as the One Map policy, Social Forestry program, and Indonesian Sustainable Palm Oil policy embody the overarching principles of a landscape approach (i.e. multiple land uses, multi-stakeholder collaboration, etc.). However, in many cases, these well-intended policies do not translate into practice at the local scale. Challenge areas include: identifying common concern entry points, clarifying rights and responsibilities, enhancing stakeholder capacity, meaningful engagement of multiple stakeholders, and identifying a negotiated and transparent change logic. We suggest that a greater commitment to these principles and the adoption of a landscape approach holds potential to enhance Indonesian policy performance and ensure that policy development is more representative of national and local concerns and practices.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".