Environmental Law Enforcement in The Perspective if Indonesia and Australia: Case Study of Forest Fires
Bibliographic record
Abstract
Forest is recognized playing a crucial role in Earth's life circle, specifically in terms the existence of human beings. As year goes by, the area of forest has been decreased significantly due to deforestation. Human beings claims that they need wider space area to live, build a house and run a factory or create a plantation to support businesses. They intentionally fires some forest areas without eco-sustainability consideration. In Indonesia, particularly, the cases of forest fires have been considered at critical level. To add, most of forest fires cases are done intentionally by the suspects. Those actions thus negatively impact a massive scope of ecosystem. Specifically speaking, it is not forest fires that brings drawbacks to the ecosystem, but the haze itself. The massive dark haze as a result of forest fires certainly pollutes the air which causes some breathing system-related diseases. Simply said, the disadvantages of forest fires have been violating human beings welfare (against humanity) and therefore be considered as a criminal action. Globally speaking, intentional forest fires have been ruled under criminal code in some countries such as Canada and Australia. Moreover, in attempt to overcome forest fires issues, Food and Agriculture Organization of the United Nations (FAO) provides a guideline for national legal drafters regulating forest fires law. Accordingly, those who are accused by criminal codes will be punished pursuant to criminal penalties regulated. In the States of Victoria (Australia), for example, criminal penalties of intentional forest fires (arson) have been effectively sentenced. From 2007-2012, 73 people were sentenced in custodial type by judges. On the contrary, although Indonesia has been regulated criminal penalties for intentional forest fires actors, they seem less powerful and effective in practice. Recently, on July 2016, Riau's forest fires case was intentionally dissolved by Indonesian National Police Officer. This fact thus raises an issue as to whether Indonesian Criminal Penalties for intentional forest fires actors have been effectively applied. This research aims to discover and solve the aforementioned issue. As a positive attempt, authors expects that this research will provide some recommendations to create more effective sentencing systems for the suspects. Authors will use sustainable forestry principles. In the end, Authors are expecting Indonesia will be able to overcome its forest fires cases effectively and more efficient.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| 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".