KEBIJAKAN INDONESIA MENOLAK MENGGUNAKAN MEKANISME AATHP JOINT EMERGENCY RESPONSE DALAM MENGATASI KEBAKARAN HUTAN DAN LAHAN 2015
Bibliographic record
Abstract
This paper aims to explain Indonesia’s rejection to resolve its 2015 forest and land fire disaster under the mechanism of Joint Emergency Response provided in the ASEAN Agreement on Transboundary Haze Pollution. The haze caused by forest and land fires in Indonesia raised threats to not only itself but also states in the region of Southeast Asia. As it was declared as a regional problem, ASEAN then responded by creating a common framework called the ASEAN Agreement on Transboundary Haze Pollution in 2002 and Indonesia was the last ratifying the agreement in 2014; more than a decade after its inception. Indonesia, however, refused to pick the AATHP Joint Emergency Response to tackle the 2015 disaster within its territory despite its most serious recurring disaster since 1997. This research applied the qualitative method with a causal correlation analysis. The research applied Charles O. Lerche and Abdul A. Said’s national interest theory. The research found that Indonesia’s rejection was driven by its national interests such as image, economy and politics which were much more important than others. Instead, Indonesia preferred the domestic efforts and bilateral cooperation to respond to it. The paper argues that the Southeast Asian regional institution is not able to offer incentives overtaking states’ domestic-oriented national interests.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".