OMNIMBUS LAW DAN PENYUSUNAN RENCANA TATA RUANG: KONSEPSI, PELAKSANAAN DAN PERMASALAHANNYA DI INDONESIA
Bibliographic record
Abstract
Fenomena permasalahan pembangunan terkait dengan perencanaan penataan ruang dan perencanaan pembangunan. Idealnya, penataan ruang dan pembangunan harus dilakukan secara terintegrasi, baik secara substansi, spasial maupun pendanaan. Secara konsep, penyusunan rencana tata ruang terkait dengan ekspresi spasial-geografis yang mencakup kebijakan perekonomian, sosial, lingkungan dan kebudayaan masyarakat. Perencanaan ruang berhubungan dengan pengembangan wilayah yang didalamnya terdapat sektor-sektor dengan sebaran sumber daya dan segala kegiatan dan permasalahannya dalam berbagai jenis dan skala. Makalah ini berusaha menjelaskan penyusunan rencana tata ruang, baik dari sisi konsepsi, pelaksanaan maupun permasalahan yang dihadapi, termasuk menyajikan permasalahan yang terjadi ditingkat tapak terkini. Metode yang digunakan adalah literature review berbasis informasi dari regulasi, jurnal, buku dan sumber lain yang relevan. Hasil analisis menunjukkan bahwa perencanaan tata ruang menghadapi tantangan adanya COVID-19 dan diterapkannya UU Cipta Kerja dan turunannya. Nuansa kental aspek “pemanfaatan” ruang dalam regulasi terkini terkait tata ruang yang dipengaruhi UU Cipta Kerja mengindikasikan bahwa pengendalian tata ruang menjadi tantangan tersendiri bagi para perencana.
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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 | 0.000 | 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 | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".