PEMBERDAYAAN ANAK PUTUS SEKOLAH MELALUI OPTIMALISASI PKBM DI DESA BATU BERIGA
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
One of the problems in Batu Beriga Village is the problem of education. From the educational statistics for the Batu Beriga village community, the highest number of graduates of 517 was elementary school graduates, then 342 junior high school graduates and 280 high school graduates. And also data on dropouts in Batu Beriga village were 10 elementary school dropouts, 15 junior high school dropouts, and 15 people dropped out of high school. The twelve-year compulsory education program with the policies and programs of the Student Operational Assistance (BOS) and the Smart Indonesia Program (PIP) set by the government. Therefore, knowledge about the importance of education and PKBM will be given to out-of-school children in Batu Beriga village with an Inspirational Talk Show on Strengthening Educational Motivation for Dropout Children to collect data on children's interest in participating in PKBM, and registering children to take part in PKBM. The method used is the counseling method with the preparation and licensing stages, the implementation stage, and the evaluation stage. Overall, this community service activity went very well and was conducive. All participants who attended calmly followed the whole activity. Until the completion of the presentation of the material by the speaker, all participants listened carefully. There were 7 school dropouts who enrolled in PKBM.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 it