Bibliographic record
Abstract
Local elections (Pilkada) still face problems, both technical and substantive. Efforts to improve have been made several times by revising the law regulating local elections (Pilkada). In the simultaneous local elections in 2020, money politics arose again. The issue of cukong democracy practices depicted capital owners' power to finance local leaders candidates to contest local elections (Pilkada). This study explores and structures the problems underlying money politics in local elections (Pilkada), which formulate alternative solutions based on logical reasoning. The exploration and formulation of issues and the preparation of alternative solutions are carried out using strong arguments to ensure the conclusions' plausibility. The practice of money politics can occur in two main areas, namely in the nomination process in the form of political dowries paid by candidates to political parties. In the campaign area, the condition of buying and selling votes carried out by candidates with voters. The practice of political dowry occurs due to the limited alternatives that a person can use to run for regional head elections. The nomination mechanism is much more difficult and costly through individual channels, so paying political dowries to political parties is the easiest and more specific option. The practice of money politics occurs because of voters' mental attitudes who are not rational, and the system of sanctions still supports the way of buying and selling votes. Alternative solutions to money politics in these two areas are compiled by doing a simulative analysis that can eliminate the practice's root causes. Decreasing the threshold for candidate submission by political parties, adopting the maximum point for political party support, reducing the number of voter support requirements for individual candidates, eliminating political dowries in local leader nominations, and simultaneously reducing costs for individual candidates. Adopting criminal sanctions for givers in the practice of buying and selling votes is an alternative solution in preventing the way of buying and selling votes in local elections (Pilkada).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".