Bibliographic record
Abstract
Tujuan studi ini mengevaluasi kebijakan intervensi stunting di Provinsi Riau tahun2018 dan 2019. Objek kajian adalah Pemerintah Kabupaten Rokan Hulu, Kabupaten Kampar danPemerintah Provinsi Riau, terutama Badan Perencanaan Pembangunan Daerah dan DinasKesehatan. Observasi lapangan dan wawancara di dua desa di Kabupaten Rokan Hulu dan satudesa di Kabupaten Kampar yang masuk dalam lokus stunting menurut Riskesdas. Metode yangdigunakan berupa mixed method dengan analisis minimum yaitu analisis kuantitatif-deskriptifyang didukung analisis kualitatif yang diperoleh dari wawancara mendalam, observasi lapangandan FGD. Pemerintah secara terstruktur mulai dari pemerintah pusat dan daerah telah melakukanberbagai upaya intervensi terhadap stunting. Pemerintah Daerah belum memasukkan stuntingsebagai indikator capaian kinerja kesehatan. Dinas Kesehatan telah mengerjakan beberapa programdan kegiatan yang berkaitan dengan intervensi stunting. Target pencapaian kinerja dinas kesehatantelah dimuat dalam perjanjian kinerja dan dilaporkan dalam Laporan Kinerja Instansi Pemerintah(LKjIP) Pemerintah Kabupaten Kampar. Pemerintah pusat telah menganggarkan dana transferkhusus melalui Dana Alokasi Khusus (DAK) 2018 dan DAK 2019. Pemerintah Provinsi Riau barumengakomodir penanganan stunting dalam dokumen perencanaan revisi RPJMD Tahun 2014-2019. Intervensi stunting Provinsi Riau telah dilakukan baik melalui strategi intervensi spesifikmaupun sensitif. Koordinasi di level Provinsi belum ditemukan adanya koordinasi lintas sektorsehingga program dan kegiatan yang ada berjalan masing-masing dengan sasaran yang berbeda.Kata kunci: kebijakan, intervensi stunting, capaian kinerja, dan perjanjian kinerja.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".