Inovasi Pelayanan Publik “SI LAKU O2T” di Kolaka Utara Sulawesi Tenggara
Bibliographic record
Abstract
Pelayanan publik yang baik dapat dikenali dan dinilai dari penyelenggaraan yang memenuhi standar pelayanan. Pada kenyataannya, pelayanan yang diberikan saat ini sering kali tidak sesuai dengan harapan masyarakat. Yang lebih memprihatinkan lagi, masyarakat belum sepenuhnya memahami layanan apa saja yang akan mereka terima sesuai prosedur. Penelitian ini dilakukan dengan menggunakan metode penelitian kualitatif pada Dinas Kependudukan dan Pencatatan Sipil Kabupaten Kolaka Utara. Teknik identifikasi informan adalah snowball sampling dan menggunakan teknik analisis data (reduksi data, penyajian data, penarikan kesimpulan). Temuan menunjukkan bahwa pelaksanaan Program Inovasi Pelayanan Publik Si Laku O2T di Kolaka Utara telah menyederhanakan persyaratan dan prosedur pelayanan serta mengurangi birokrasi melalui penetapan standar pelayanan. Secara keseluruhan, pelaksanaan layanan berjalan baik dari berbagai aspek seperti operasional layanan, waktu penyelesaian layanan, biaya layanan, peralatan dan infrastruktur layanan, serta kemampuan penyedia layanan. Namun waktu penyelesaian pelayanan harus lebih diperhatikan karena waktu sebenarnya yang dibutuhkan tidak sesuai dengan waktu yang ditentukan dalam kriteria pelayanan.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.029 | 0.004 |
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".