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.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".