Kajian kelengkapan instrumen pengendalian pemanfaatan ruang Ibu Kota Nusantara
Bibliographic record
Abstract
Regulasi pengendalian pemanfaatan ruang di Ibu Kota Nusantara (IKN) mengembangkan sejumlah istilah baru yang penting untuk diteliti kelengkapan dan kesesuaiannya dengan standar normatif di Indonesia agar rencana yang dituangkan dalam rencana induk dapat terimplementasikan dengan baik. Tujuan dari studi ini adalah mengidentifikasi kelengkapan instrumen pengendalian pemanfaatan ruang di IKN yang digunakan untuk mewujudkan rencana pembangunan IKN. Pada studi ini metode pengambilan data dilakukan melalui desk study mengenai instrumen pengendalian pemanfaatan ruang yang ada di wilayah IKN dan yang berlaku di Indonesia. Penelitian ini merupakan penelitian kualitatif deskriptif dengan pendekatan normatif komparatif serta metode analisis yang akan digunakan adalah analisis deskriptif, analisis konten, dan analisis komparatif antara instrumen pengendalian pemanfaatan ruang di IKN dengan instrumen pengendalian pemanfaatan ruang di Indonesia. Berdasarkan hasil analisis yang dilakukan disimpulkan bahwa terdapat klasifikasi instrumen pengendalian pemanfaatan ruang di IKN yang menggunakan instrumen pengendalian pemanfaatan ruang nasional, instrumen pengendalian pemanfaatan ruang khusus IKN setara dengan instrumen pengendalian pemanfaatan ruang nasional dan instrumen pengendalian pemanfaatan ruang khusus IKN.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 0.005 |
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".