Public Information Disclosure Policy in Climate Change Adaptation: A Comparative Study of Indonesia and South Korea
Bibliographic record
Abstract
The principle of transparency in information disclosure plays a crucial role in global climate governance, as it seeks to enhance accountability and improve the effectiveness of climate change mitigation efforts.The disclosure of public information regarding climate change policies, such as Nationally Determined Contributions (NDC) documents, plays a vital role in facilitating the engagement of the public and various stakeholders.In Indonesia, the accessibility of NDC documents remains a significant challenge, impacting the public's comprehension of government policies related to climate change mitigation.In comparison, South Korea, the first nation in Asia to implement a Right to Information Act in 1996, has demonstrated a more robust dedication to transparency and the disclosure of NDC documents.This study examines the management of information disclosure in the NDC policies of Indonesia and South Korea, focusing on its implications for public participation and the attainment of emission reduction targets.The findings indicate that although both nations demonstrate a robust dedication to addressing climate change, Indonesia must enhance its engagement with communities and the private sector by improving access to information, fostering broader public participation, and aligning NDC policies with national and regional development strategies.A transparent and inclusive approach can enhance the effectiveness of climate change policies, thereby accelerating the attainment of mitigation and adaptation targets related to climate change.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".