Forest certification, state regulation and community empowerment: complementarity in seeking a viable solution to forest degradation in Indonesia?
Bibliographic record
Abstract
For developing countries like Indonesia, the advantages enjoyed by developed countries – of political stability and highly regulated systems of land tenure and ownership – are elusive, leading to a situation in which state intervention in forest governance is met with resistance and faces significant hurdles. Recognizing the challenges facing implementation of Indonesia's current systems of certification, and the failure thus far of government efforts to stem illegal forestry activity, this article examines the influence of certification on sustainable forest management (SFM) in Indonesia. In particular, the question of how certification requirements interact with both the domestic regulatory framework and expectations for community participation and engagement is considered. The article begins by reviewing Indonesian efforts to implement SFM, as well as the basis of certification systems; before examining Indonesian experience with forest certification, drawing both upon previously published studies and field research by the authors. Finally, the article considers complementarity in government, private and community initiatives in SFM and how regulatory reform in support of a more participatory approach could contribute to achieving these goals. The development of the Indonesian voluntary forestry certification process shows that both certification schemes and government regulation provide advantages and disadvantages in improving the sustainability of forest management and in controlling illegal activities. An increased role for communities, small-scale producers and traditional forest users appears important in the Indonesian context, providing additional options and capacity for sustainable forest management.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".