PEMBERDAYAAN MASYARAKAT DESA MELALUI METODE KERJASAMA DENGAN AKADEMISI PROGRAM KULIAH KERJA NYATA
Bibliographic record
Abstract
Kuliah Kerja Nyata Tematik Terintegrasi merupakan salah satu program pengabdian mahasiswa yang memiliki tujuan untuk mengembangkan, meningkatkan mutu sumber daya dan mencapai efektivitas program pembangunan yang ditandai dengan semakin baiknya kualitas kehidupan masyarakat pada bidang agama islam, pendidikan, kesehatan, ekonomi, hukum dan teknologi. Kegiatan KKN dibagi menjadi beberapa tahap, yaitu survey dan observasi ke desa sasaran, workshop, pendekatan sasaran program, realisasi program, monitoring dan evaluasi kegiatan, dan lokakarya hasil KKN. Kegiatan ini dilaksanakan mulai tanggal 06 Agustus - 05 September 2019. Desa Warga Jaya merupakan salah satu desa DiWilayah Kecamatan Cigudeg Kabupaten Bogor yang menjadi desa sasaran KKN dengan luas wilayah 772, 38 Ha yang terbagi dalam 6 Dusun, 13 RW dan 43 RT. Adapun beberapa program KKN yang dilaksanakan meliputi Taman Belajar (Tabel), Mengajar di PAUD,Mengajar di SDN, Penyuluhan Ekonomi, Re-branding product, Penyuluhan Kesehatan Sikat gigi dan Cuci tangan, Jum’at bersih, Peringatan Hari Kemerdekaan RI, Seminar Hukum, Seminar Teknologi, Pembuatan dan pemasangan plang jalan, Pembuatan dan pemasanganplakat RT, Pembuatan bak sampah dan Pembuatan Penerangan jalan. Secara umum, hasil kegiatan telah sesuai dengan rencana yang telah ditetapkan, walaupun ada beberapa hambatan yang dialami
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.049 | 0.008 |
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".