Partisipasi Masyarakat Kota Medan dalam Upaya Bela Negara Terhadap Pertahanan dan Keutuhan NKRI
Bibliographic record
Abstract
Penelitian ini membahas mengenai partisipasi masyarakat dalam Bela Negara \nterhadap Pertahanan dan Keutuhan NKRI. Penelitian ini bertujuan untuk menjawab \nfocus masalah mengenai partisipasi masyarakat dalam Bela Negara terhadap \nPertahanan dan Keutuhan NKRI yang menghasilkan penjabaran sebagai berikut: 1) \nBagaimana partisipasi masyarakat Kota Medan dalamu paya Bela Negara terhadap \nPertahanan dan keutuhan NKRI di Kelurahan Sei Sikambing D Kecamatan Medan \nPetisah? 2)Bagaimana upaya Pemerintah Kota Medan di Kel. Sei Sikambing D Kec. \nMedan Petisah dalam meningkatkan kesadaran masyarakat dalam upaya bela negara ? \nPenelitian menggunakan penelitian lapangan dengan metode kualitatif \ndeskriftif. Penulis membatasi analisis ini agar lebih jelas dan terfokus yaitu hanya \npada partisipasi masyarakat, serta upaya pemerintah dan lokasi penelitian dilakukan \ndi Kelurahan Sei Sikambing D kecamatan Medan Petisah Kota Medan. \nHasil temuan penelitian menghasilkan bahwa partisipasi masyarakat dalam \nupaya bela negara yaitu Cinta terhadap tanah air, sadar berbangsa dan bernegara, \nyakin Pancasila sebagai ideologi negara, dan rela berkorban untuk bangsa dan negara. \npembinaan-pembinaan, pendidikan dan pelatihan awal bela negara (diklat awal bela \nnegara).
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.064 | 0.016 |
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".