POLICY BRIEF: OPTIMALISASI INTEGRITAS DAN AKSELERASI PELAYANAN PPDB 2024: EVALUASI BERBASIS DATA KCD WILAYAH IX (KABUPATEN INDRAMAYU & MAJALENGKA)
Bibliographic record
Abstract
Policy Brief ini menyajikan evaluasi mendalam mengenai pelaksanaan Penerimaan Peserta Didik Baru (PPDB) Tahun 2024 di wilayah kerja Cabang Dinas Pendidikan Wilayah IX (Kabupaten Indramayu dan Kabupaten Majalengka), Provinsi Jawa Barat. Dokumen ini disusun berdasarkan hasil analisis data lapangan dan bimbingan teknis PPID serta Tim Pengaduan yang dilaksanakan pada Mei 2024. Poin Utama Analisis: · Tren Partisipasi Digital: Sebanyak 75% pendaftar telah melakukan pendaftaran secara mandiri, yang menunjukkan tingkat literasi digital masyarakat yang cukup baik di Wilayah IX. · Tantangan Data Afirmasi: Ditemukan adanya residu data berupa status "Tidak Aktif" dan "Anomali" pada jalur KETM (Keluarga Ekonomi Tidak Mampu) yang memerlukan sinkronisasi harian dengan database DTKS. · Integritas Pelayanan: Pentingnya implementasi Tanda Tangan Elektronik (BSrE) secara menyeluruh untuk menjamin kekuatan hukum dokumen dan mencegah manipulasi data pasca-pengumuman. Rekomendasi Strategis: 1. Akselerasi pelayanan informasi melalui pengembangan dasbor statistik real-time di tingkat satuan pendidikan guna mengurangi beban kerja helpdesk. 2. Penguatan tata kelola data melalui audit berkala yang melibatkan tenaga ahli profesional (fungsional dengan kualifikasi pendidikan tinggi) untuk menjamin akurasi statistik dan kedalaman akademik. 3. Formalisasi hasil verifikasi melalui Berita Acara yang tersertifikasi secara digital untuk memitigasi risiko gugatan hukum.
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How this classification was reachedexpand
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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.016 |
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; both teacher heads agree on what is shown here.
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".