Law Enforcement and Fulfillment of the Right to a Healthy Environment Related to Forest Burning During the Covid-19 Pandemic in Indonesia
Bibliographic record
Abstract
Law enforcement against forest burning during the Covid-19 pandemic was carried out extraordinary. The state is obliged to fulfill the rights of citizens to a good and healthy environment, to overcome forest fires. The country must also restore environmental quality from smoke pollution, as well as the government's commitment to tackling climate change due to forest fires during the Covid-19 pandemic. In this article, the problem is how is law enforcement against forest burning during the Covid-19 pandemic? and how to fulfill community rights to a healthy environment related to forest burning during the Covid-19 pandemic? The method used is a variety of literature and information media during a pandemic related to forest burning, as well as the Law on Environment and Forest Management, using qualitative juridical analysis. In conclusion, first, law enforcement against forest burning during the Covid-19 pandemic is carried out extraordinarily by implementing a system of heavy sanctions, revoking permits, optimizing the recovery costs for Covid-19 response to the results of corporate efforts to guarantee the economy of citizens and donations to the state to address the handling of Covid-19, and to involve indigenous peoples in forest areas with local wisdom that contribute to reducing carbon emissions, so that climate change can be resolved during the Covid-19 pandemic; second, the fulfillment of people's rights to a healthy environment related to forest burning during the Covid-19 pandemic is a state obligation guaranteed by the constitution, maintaining good environmental quality, and Indonesia's commitment to reducing carbon emissions and forest fires due to climate change, as well as law enforcement, oriented towards fulfilling the rights to the environment and health of citizens.
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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 | 0.000 |
| 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".