PENGAWASAN PENERIMAAN PESERTA DIDIK BARU JALUR KELUARGA EKONOMI TIDAK MAMPU
Bibliographic record
Abstract
This research is motivated by the existence of irregularities in the process of Accepting New Students (PPDB) for the Family of Poor Economic Paths in every State Senior High School in Majalengka Regency so that the implementation is not on target. Based on the background, the researcher needs to know how the monitoring process of the Regional Education Office Branch of Majalengka District IX in the implementation of the program. The research method used is descriptive method through a qualitative approach. Data collection techniques carried out by field studies, literature studies, observations, interviews, and documentation. The results of the research can be seen that in the implementation of PPDB JKETM SMA there are still irregularities in the PPDB SMA in Majalengka Regency, including the lack of awareness from parents / guardians of CPDB on what rights and obligations each CPDB has, there are no specific sanctions imposed on each parties who misuse the PPDB system so that no deterrent effect is received by the parties concerned, school supervisors have not carried out the supervisory function as a whole but only preventively through socialization and only receive reports from the implementing committee, so that there are deviations from the prospective new students (CPDB) ) which is not known by the school supervisor and ultimately impedes the effectiveness of the high school PPDB supervision. Another finding is that there is no openness of supervision through the media by the Regional Education Office Branch of Majalengka District IX so that reciprocal supervision from the community and schools has not been carried out.
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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 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.005 |
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".