EVALUASI MANAJEMEN PILKADA KOTA METRO DI ERA PANDEMI COVID-19
Bibliographic record
Abstract
Penelitian ini bertujuan: 1) Untuk mengetahui struktur realitas permasalahan yang dihadapi oleh aparatur KPU Kota Metro dalam manajemen Pilkada Kota Metro yang dilaksanakan oleh KPU Kota Metro tahun 2020; 2) Untuk mengetahui format perencanaan, koordinasi, implementasi dan pengawasan eksisting dalam setiap tahapan Pilkada Kota Metro tahun 2020; 3) Untuk mengetahui model manajemen pelaksanaan Pilkada Kota Metro yang dilaksanakan oleh KPU Kota Metro tahun 2020 agar dihasilkan Pilkada yang berkualitas. Tehnik analisa data yang digunakan dalam penelitian ini adalah Analisis Pendekatan Fishbone Ishikawa dan Analytical Hierarchy Process (AHP). Hasil penelitian menunjukkan Faktor atau Kriteria yang dianggap paling penting dalam Evaluasi Manajemen Pilkada Kota Metro adalah Partisipasi Pemilih yang sangat berpengaruh, sedangkan kriteria sistem informasi menjadi kriteria yang dianggap paling tidak penting dan berpengaruh karena tidak banyak berdampak pada pelaksanaan Pilkada Kota Metro.
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 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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".