Achieving Public Information Transparency in The Dissemination of Local Regulations
Bibliographic record
Abstract
Comparing the disclosure of public information regarding the dissemination and publication to the public of newly enacted rules, setting Indonesia and the Netherlands as parameters is interesting as the legal cultures of both countries are intertwined and legal perspectives are constantly evolving. Despite differences in the rule of law, both countries have similar regulatory frameworks. This research uses a normative juridical writing method with a literature study to analyze the relationship between good governance and public information disclosure, including definitions, indicators, objectives, and important factors that influence the concept. The aim is to serve as a comparison parameter for a more efficient information disclosure mechanism. In Indonesia, the role of DPRDs is closely related to the issue of public information disclosure, as DPRDs have a dominant role in authority. Meanwhile, in the Netherlands, the local government takes the lead in setting regulations. Nonetheless, both countries emphasize the importance of checks and balances through the establishment of RvD and KIP, to oversee and integrate the aspirations of the community. The results of this study suggest that public information disclosure is important in both Indonesia and the Netherlands, as demonstrated by regulations that prioritize transparency, accountability, public participation, responsiveness, and the rule of law in governance. This commitment to openness aims to bridge the gap between public expectations and government actions. And in conclusion, the harmony between the process of forming regulations involving the DPRD as a representative of the people's voice and information disclosure is interrelated in public services towards the achievement of good governance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.054 | 0.111 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".