Faktor-Faktor Penyebab Kekumuhan Di Kelurahan Kapasari Kecamatan Genteng, Kota Surabaya
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<p class="Abstract">Kawasan permukiman kumuh di Kelurahan Kapasari memiliki karakteristik kondisi hunian padat, penggunaan lahan yang tidak sesuai fungsi, dan tidak ada RTH. Kawasan ini terdiri dari permukiman kumuh pinggiran rel dengan status lahan illegal (squatter area) dan permukiman kumuh tengah kota (slum area). Keberadaan kawasan permukiman kumuh akan berdampak pada penurunan kualitas dan visualisasi buruk bagi kawasan pusat Kota Surabaya<em>.</em> Artikel ini merupakan bagian dari penelitian terkait perumusan arahan penataan kawasan permukiman kumuh di Kelurahan Kapasari. Pada artikel ini akan dibahas dan didapatkan faktor penyebab kekumuhan <em>squatter area </em>dan <em>slum area </em>melalui teknik analisis Delphi. Hasil penelitian menunjukkan terdapat 8 faktor penyebab kekumuhan di <em>squatter area</em>, yaitu tingkat pendidikan, aksesibilitas ke lokasi kerja, tingkat pendidikan, tingkat migrasi masuk, kualitas sarana dan prasarana, tingkat kesadaran masyarakat, kepadatan bangunan tinggi, dan peran serta pemerintah. Sedangkan <em>slum area </em>terdapat 5 faktor yang menyebabkan kekumuhan, yaitu aksesibilitas ke lokasi kerja, tingkat migrasi masuk, tingkat kesadaran masyarakat, kepadatan bangunan tinggi, dan peran serta pemerintah <em><br /></em>
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.021 |
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 it