Bibliographic record
Abstract
Penelitian ini melihat bagaimana infrastruktur jalan mempengaruhi kriminalitas di pedesaan Indonesia. Sejak dikeluarkannya Undang-Undang No. 6 Tahun 2014 tentang Desa, terjadi pembangunan masif infrastruktur jalan untuk mengembangkan perekonomian desa. Namun, pembangunan jalan tersebut menimbulkan adanya eksternalitas negatif, yaitu kriminalitas. Dengan model regresi logistik, penelitian ini menganalisis data Potensi Desa tahun 2006-2018 di seluruh Indonesia. Hasil menunjukkan bahwa pembangunan infrastruktur jalan di pedesaan umumnya beriringan dengan peningkatan peluang kriminalitas sekitar 1,3-1,5 kali lebih tinggi. Setelah pembangunan masif, terindikasi peningkatan ekonomi yang berdampak pada turunnya kriminalitas di desa-desa terpencil. Temuan ini mendukung literatur infastruktur jalan dan aksesibilitas dapat memberikan peluang terjadinya kriminalitas.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".