Pengaruh Kebijakan Restriksi Pemberian Hibah Bansos terhadap Politisasi Anggaran oleh Calon Incumbent pada Pilkada Kabupaten/Kota Tahun 2017
Bibliographic record
Abstract
Penelitian ini bertujuan untuk menganalisis sejauh mana kebijakan restriksi pemberian hibah dan bantuan sosial dapat mencegah adanya political budget cycles (PBC) di era pemilihan kepala daerah pada daerah-daerah yang memiliki calon incumbent, dengan mengambil kasus pelaksanaan Pilkada Serentak Tahun 2017 di 94 kabupaten/kota di Indonesia. Dengan menggunakan fixed effect regression atas data panel selama periode tahun 2014-2018, penelitian ini menemukan bahwa tidak ada perbedaan yang signifikan terkait pertumbuhan anggaran belanja hibah dan bantuan sosial pada tahun politik 2016- 2017 antara kabupaten/kota yang memiliki calon incumbent dan tidak. Terdapat indikasi bahwa kebijakan restriksi pemberian hibah dan bantuan sosial yang diberlakukan di tahun 2016 memiliki korelasi positif dengan tidak adanya political budget cycles (PBC) di daerah-daerah yang memiliki calon incumbent.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.044 | 0.006 |
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".