EVALUASI PENGELOLAAN DANA BANTUAN OPERASIONAL SEKOLAH (BOS) SD NEGERI HARAPAN MAKMUR KABUPATEN MUSI RAWAS KECAMATAN MUARA LAKITAN
Bibliographic record
Abstract
The School Operational Assistance Fund (BOS) is a government program which is basically for the provision of non-personnel operating costs for basic education units as the implementer of the compulsory education program. The analytical method used is descriptive qualitative analysis method. The results show that (1) Planning for BOS funds is in accordance with the 2019 BOS Technical Guidelines because it is seen from Government Regulation Number 3 of 2019 concerning education funding from BOS funds obtained by SD Negeri Harapan Makmur which comes from the State Budget (APBN) for 2019 is IDR 157,600,000 / year, - (2) Implementation of BOS funds, especially the allocation is in accordance with the 2019 Technical Guidelines for BOS, because in the allocation, filling in dapodikdasmen data (3) the distribution of BOS funds is in accordance with the 2019 technical guidelines, namely by going through two stages (4 ) The use of BOS funds is in accordance with the 2019 Technical Guidelines for BOS, in the form of 11 components that can be funded by BOS funds. (5) not all of the supervision has been carried out due to the lack of direct supervision from the school committee (6) Reporting the accountability of BOS funds is in accordance with the 2019 Technical Guidance, only there are no banners information, and for external reports it is in accordance with the reports made every quarter.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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 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".