Pilkada 2015 and Patronage Practice among Bureaucrat in West Kalimantan, Indonesia
Bibliographic record
Abstract
Majority studies on electoral dynamics in Indonesia are reinforce patronage as a pattern of relationships between candidates, winning team, and voters. One of winning team element which have little attention from scholars is bureaucrat. Although, normatively, bureaucrats are required to neutral in all type of general election, but in fact bureaucrats is very involved deeply in general elections. Based on empirical research in Sintang District and Ketapang District, West Kalimantan, Indonesia, we found that bureaucrats have a significant role as a winning team in pilkada (direct election for local leader). Patronage is a keyword to explain political relation between bureaucrats and candidate in pilkada. This situation was triggered by the fact that there are many candidates who have social background as civil servant and, consequently, have direct access to bureaucracy. Bureaucrats have high motivation to participate in pilkada as a broker due to protecting their vested interest. In our cases, the vested interest of bureaucrat is career stability which is promising additional personal revenue and social status. Consequently, staffing (circulation of position) within bureaucracy does not followed by auction mechanism (merit system), but following nepotism mechanism (spoil system). The winner of pilkada is socially pressured to accommodate all bureaucrats who has become their winning team. Finally, we discuss our finding and propose future agenda research to understanding this phenomenon.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".