The Interplay of Law, Local Wisdom, and Carbon Policy: Historical Foundations of Indonesia’s Environmental Regulation
Bibliographic record
Abstract
Carbon emissions are one of the leading causes of climate change. Carbon consumption or carbon footprint has been a colossal topic because sustainability can be achieved through carbon emission reduction, as carbon emissions are one of the leading causes of climate change. Indonesia is actively working on environmental conservation, but it continues to face various challenges that require attention. Indonesia's commitment to reducing carbon emissions can be shown through its effort to shift toward low-carbon development. Implementing natural resource management laws in Indonesia has not gained popularity, as they are often viewed as unsupportive of environmental sustainability. The regulation is continuously updated and adjusted to address emerging environmental issues. This research aims to explore how local wisdom contributes to forest conservation using qualitative methodology in the Seruyan District, Central Kalimantan. While regulation on carbon trading in forestry is still ongoing, it can be enriched which states that the community has the same rights and opportunities to actively participate in environmental protection and management. The role of society can be providing advice, opinions, suggestions, objections, and complaints. In this case, the society is the local community that lives near the forest. Incorporating their knowledge of preserving nature and preventing forest fires into the policy can be beneficial.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.030 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| 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".