KAJIAN PENYUSUNAN RTBL SUB BWP PRIORITAS PADA BWP MALANG TENGAH
Bibliographic record
Abstract
Kota Malang merupakan wilayah strategis dan perkembangan wilayahnya sangat pesat. Perkembangan wilayah Kota Malang harus di imbangi dengan desain perencanaan pembangunan dan lingkungan, sehingga dapat menjadi daya tarik wilayah, memperlancar pelaksanaan tugas di bidang pemerintahan dan peningkatan pelayanan masyarakat.Penelitian ini bertujuan sebagai kajian Rencana Tata Bangunan Lingkungan (RTBL) pada sub Bagian Wilayah Perencanaan (BWP) Malang Tengah. Metode yang digunakan dalam penelitian ini menggunakan mixed method dengan pendekatan kualititatif dan kuantitatif. Pendekatan kuantitatif digunakan untuk analisis indikator kajian RTBL berupa prospek pertumbuhan ekonomi, daya dukung fisik lingkungan, daya dukung prasarana dan fasilitas lingkungan, dan analisis mikro kawasan. Pendekatan kualitatif digunakan untuk analisis kawasan makro Kota Malang. Hasil kajian RTBL Sub BWP Malang Tengah didasarkan atas beberapa analisis, meliputi analisis kawasan makro yang meliputi sejarah arsitektur Kota Malang, prospek pertumbuhan ekonomi, daya dukung fisik dan lingkungan, daya dukung prasarana dan fasilitas lingkungan. Analisis kawasan mikro meliputi analisis kesesuaian dan kelayakan lahan, penggunaan lahan, analisis lahan makro, dan analisis koofisien bangunan. Masing-masing analisis yang digunakan memberikan penilaian mengenai penyusunan RTBL BWP Malang Tengah.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".