ANALISIS PARTISIPASI MASYARAKAT DALAM PEMILIHAN UMUM CALON LEGISLATIF TAHUN 2009 DI KECAMATAN BENGKALIS KABUPATEN BENGKALIS
Bibliographic record
Abstract
Penelitian ini bertujuan untuk mengetahui seberapa besar partisipasi \nmasyarakat dalam Pemilihan Calon Anggota Legislatif Kecamatan Bengkalis \nKabupaten Bengkalis priode 2009-2014 di Kecamatan Bengkalis Kabupaten \nBengkalis dengan menggunakan metode pengumpulan data quisioner, \nwawancara, dan observasi. Tehnik analisa data yang digunakan yaitu teknik \nkualitatif dengan analisa dan pengamatan secara langsung terjun kemasyarakat. \nHasil penelitian di atas menunjukan bahwa tingginya tingkat partisipasi \nmasyarakat dalam pemilihan Calon Anggota Legislatif Kecamatan Bengkalis \npriode 2009-2014, di Kecamatan Bengkalis dapat dilihat dari partisipasi \nmasyarakat mulai dari pendaftaran, Kampanye, Pemilihan dan Penghitungan \nSuara. \nPenelitian ini juga menyarankan agar Aparatur Pemerintah Kecamatan \nBengkalis agar mendorong masyarakat untuk berpartisipasi dalam setiap proses \npemilihan (hak suara) serta menindaklanjuti setiap usul dan saran masyarakat \nsehingga pemilihan kedepanya lebih berskala sukses.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.044 | 0.008 |
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".