PELATIHAN PEMANFAATAN GOOGLE FORM PADA APARAT DAN PERANGKAT DESA NEGARA SAKA KABUPATEN PESAWARAN DALAM RANGKA PENINGKATAN PROGRAM KERJA DESA
Bibliographic record
Abstract
Data merupakan keterangan objektif tentang suatu fakta baik dalam bentuk kualitatif maupun gambar visual yang diperoleh melalui observasi langsung maupun dari yang sudah terkumpul. Basis data dan informasi yang valid dan terukur maka proses perencanaan pembangunan yang baik dan komprehensif akan menjadi titik penting dalam keberhasilan pembangunan desa. Desa Negara Saka yang terletak di Kabupaten Pesawaran belum memilikinya data desa terbaru sehingga hal tersebut menyebabkan terhambatnya kpala desa dalam menentukan program kerja yang sesuai. Dengan memanfaatkan fasilitas internet untuk memudahkan aparat desa dalam pengumpulan data yang cepat dan akurat, sehingga memudahkan pihak kepala desa dalam menentukan program untuk perkembangan dan pembangunan desa yang sesuai. Oleh karena itu, pada pengadian ini, Fakultas Ekonomi melakukan kegiatn pegabdian dengan mengenalkan dan memanfaatkan fasilitas Google Form dalam pengumpulan data.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.106 | 0.078 |
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".