DEGRADASI FUNGSI LEGISLASI DPRD DALAM SENTRALISASI KEBIJAKAN PENETAPAN RENCANA TATA RUANG WILAYAH DI KABUPATEN/KOTA
Bibliographic record
Abstract
The issuance of Law Number 11 of 2020 concerning Job Creation has broad implications in various fields/fields, one of which is in the field of determining Regional Spatial Plans in Regencies/Cities. The a quo Law centralizes the policy on the Determination of Regional Spatial Plans in Regencies/Cities. This article addresses three main issues. First, how is the authority of the Regional Government in making arrangements related to the Regency/City Regional Spatial Planning. Second, why centralization is carried out in the determination of Regency/City Regional Spatial Plans. Third, how is the existence of DPRD in implementing the legislative function in determining the Regency/City Spatial Planning. The provisions contained in Law Number 11 of 2020 have an impact on the legislative function of the DPRD which is experiencing degradation. This article uses a normative research method with a conceptual approach and a statutory approach. With the existence of this work copyright law, the regulatory authority has become centralized, although the regions are still given several roles and authorities.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| 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".