Bibliographic record
Abstract
Penelitian ini bertujuan untuk mengetahui pola permukiman serta mengkaji faktor-faktor perkembangan permukiman di Kecamatan Rambatan, Kabupaten Tanah Datar. Jenis penelitian ini adalah deskriptif kuantitatif dengan populasinya adalah kepala keluarga di Kecamatan Rambatan. Penentuan ukuran sampel menggunakan rumus Slovin sehingga diperoleh sampel sebanyak 75 titik permukiman. Teknik pengambilan sampel yang digunakan adalah simple random sampling. Teknik pengumpulan data menggunakan data sekunder dengan teknik analisis data yaitu analisis tetangga terdekat. Analisis tetangga terdekat dilakukan dengan cara pemberian titik pada setiap variabel yang digunakan dalam penelitian. Faktor-faktor perkembangan permukiman dalam penelitian ini diukur dengan faktor fisik dan faktor sosial. Faktor fisik berupa faktor alam, faktor letak, faktor transportasi dan aksesibilitas. Faktor sosial berupa faktor pertumbuhan penduduk dan faktor ekonomi. Hasil analisis faktor-faktor perkembangan permukiman diketahui bahwa pola permukiman Kecamatan Rambatan termasuk dalam klasifikasi clustered (mengelompok), faktor fisik dan faktor sosial menjadi pengaruh lambatnya perkembangan permukiman di Kecamatan Rambatan.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.258 | 0.103 |
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".