Implementasi PERMENDIKBUD No. 14 Tahun 2018 terhadap Peneriman Peserta Didik Baru Berdasarkan Zonasi Sekolah
Bibliographic record
Abstract
Education is something that is important can provide knowledge that can improve the characteristics of life, both in personal life, community life, and state life. A New Student Admission, often referred to as PPDB, isan annual activity which is the selection stages for prospective new students organized by school-level committees under the supervision and coordination of the Office of Education. This year, PPDB uses a new system, the zoning system, which aims to equalize students. The purpose of this study was to describe how the implementation of Permendikbud number 14 in 2018 and what are the factors inhibiting the implementation of Permendikbud number 14 in 2018 on the admission of new students based on zoning. This research was designed using an empirical legal research approach. The results of this study indicated that the implementation of Permendikbud No. 14 of 2018 in High Schools / Kejuraan especially in the South Kuta area has not been running effectively because there was one school that received protests from students' parents which resulted in the process of hiring new students being hindered. In addition, this study also showed that the inhibiting factors for the implementation of Permendikbud No. 14 of 2018 on the admission of new students based on zoning consisted of internal factors, namely the committee was less selective in examining student requirements and the application provided was experiencing interference, and external factors namely location, community mainset, and domicile certificate.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".