Penguatan Desa Sebagai Garda Terdepan dalam Pencegahan dan Pemberantasan Penyalahgunaan dan Peredaran Gelap Narkoba (P4GN)
Bibliographic record
Abstract
Indonesia menghadapi tantangan serius penyalahgunaan dan peredaran gelap narkotika yang kian meluas ke wilayah pedesaan. Policy paper ini bertujuan menganalisis efektivitas pelaksanaan Program Pencegahan dan Pemberantasan Penyalahgunaan dan Peredaran Gelap Narkotika (P4GN) di tingkat desa serta merumuskan strategi integratif untuk pengarusutamaannya. Kajian ini menggunakan pendekatan kualitatif deskriptif melalui studi literatur, telaah regulasi, dan analisis data sekunder dari BNN, UNODC, serta Kemendesa PDTT. Analisis diperkuat dengan Problem Tree Analysis dan metode USG (Urgency, Seriousness, Growth) untuk menentukan prioritas masalah. Hasil analisis mengidentifikasi tiga persoalan utama: (1) regulasi dan koordinasi yang belum terpadu, (2) kapasitas aparatur desa yang terbatas, dan (3) rendahnya pemanfaatan dana desa untuk P4GN---hanya 1,92% desa yang mengalokasikannya pada tahun 2024. Kondisi ini menyebabkan desa belum berfungsi optimal sebagai garda terdepan pencegahan narkoba berbasis komunitas. Disimpulkan bahwa efektivitas P4GN di desa memerlukan kebijakan yang operasional, kolaboratif, dan berkelanjutan dengan dukungan lintas sektor. Penguatan tata kelola desa, peningkatan kapasitas aparatur, serta optimalisasi sumber daya menjadi kunci transformasi desa dari wilayah rentan menjadi benteng ketahanan masyarakat terhadap narkotika.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.045 | 0.006 |
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".