Kajian Kriminalitas di Kota Vancouver, BC, Kanada : Analisis Spasial-Temporal Tahun 2016-2018
Bibliographic record
Abstract
Kota Vancouver merupakan salah satu kota yang paling berkembang di Amerika Utara dengan kejadian kriminalitas yang sangat dinamis. Penelitian ini mengkaji kejadian kriminalitas properti ( BNE Commercial, BNE Residential, Theft from Vehicle, Theft of Vehicle ) yang terjadi dalam rentang tahun 2016-2018 dengan beberapa metode statistik, pemetaan, dan korelasi. Hasilnya menunjukkan bahwa (1) melalui Optimized Hotspot Analysis , diketahui kriminalitas paling banyak terjadi pada sisi utara kota, dengan pengecualian BNE Residential. (2) Perubahan musim tidak mempengaruhi tingkat kriminalitas. (3) Terdapat kecenderungan viktimisasi berulang pada setiap sub kategori kriminalitas properti. (4) Ditemukan hubungan faktor sosial-ekonomi terhadap tingkat kriminalitas properti di Kota Vancouver
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".