PEMETAAN KERAWANAN KRIMINALITAS DI WILAYAH HUKUM KEPOLISIAN RESORT (POLRES) KOTA PAYAKUMBUH TAHUN 2014
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
Tujuan penelitian ini untuk: (1) Mengetahui jenis tindak kriminalitas dan persentase setiap jenis tindak kriminalitas yang terjadi di Wilayah Hukum Kepolisian Resort (POLRES) Kota Payakumbuh tahun 2014. (2) Memetakan kerawanan kriminalitas di Wilayah Hukum Kepolisian Resort (POLRES) Kota Payakumbuh tahun 2014.Jenis penelitian deskriptif kuantitatif .Teknik analisis data dilakukan dengan menggunakan metode analisis statistik dan teknik overlay pada peta.Hasil penelitian menemukan bahwa: (1) Jenis tindak kriminalitas meliputi jenis kriminal yaitu: Curas (pencurian dengan kekerasan) sebanyak 88 kasus. Curat (pencurian dengan pemberatan) sebanyak 242 kasus. Curanmor (pencurian sepeda bermotor) sebanyak 87 kasus. Anirat (penganiayaan berat) sebanyak 176 kasus dan Aniring (penganiayaan ringan) sebanyak 160 kasus. Persentase perbandingan antara berbagai jenis kejahatan atau tindak kriminalitas tertinggi sebesar 38,5% untuk Curat di Kecamatan Lampasi Tigo Nagari dan yang paling rendah sebesar 6,1% untuk Curanmor terjadi di Kecamatan Luhak. (2) Kerawanan kriminalitas tertinggi terjadi di Kecamatan Payakumbuh Barat sebesar 49 kali per satuan penduduk dan terendah di Kecamatan Lampasi Tigo Nagari sebesar 4 kali per satuan penduduk.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.013 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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