ANALISIS EFEKTIVITAS AKSI KONVERGENSI/INTEGRASI STUNTING DI WILAYAH KABUPATEN ACEH JAYA
Bibliographic record
Abstract
Gerakan penurunan stunting di Kabupaten Aceh Jaya dimulai pada Tahun 2020 berdasarkan Keputusan Bupati Aceh Jaya Nomor 307 Tahun 2020 tentang Tim Pengentasan dan Penanganan Malnutrisi Terintegrasi Kabupaten Aceh Jaya dan mulai fokus pada Pencegahan dan Penangulangan malnutrisi (Stunting/Gizi Akut). Tujuan kajian ini adalah untuk menganalisa efektif atau tidaknya pelaksaaan program 8 (delapan) aksi Konvergensi/Integrasi Stunting yang sudah dirumuskan guna menuunkan angka stunting di Kabupaten Aceh Jaya. Kajian menggunakan pendekatan kualitatif, data dianalisa dengan menggunakan model Miles and Huberman. Penelitian ini menemukan bahwa Realisasi pelaksanaan program 8 (delapan) Aksi Konvergensi/Integrasi Stunting di Kabupaten Aceh Jaya sudah berjalan dengan efektif. Persentase penderita stunting di Kabupaten Aceh Jaya menunjukkan tren penurunan dari persentase 20% menjadi 12,6 %. Hal ini menunjukkan adanya tren penurunan dari tahun sebelumnya. Ini merupakan hasil positif dari kerja bersama yang sudah dilakukan oleh Sekber Bangraja dalam melakukan intervensi, baik secara spesifik maupun intervesi senstif kepada sasaran anakpenderita malnutrisi (stunting/gizi akut).
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".