The Interpretation of Structured, Systematic, and Massive Violations in the 2019 Presidential Election Dispute at the Indonesian Constitutional Court
Bibliographic record
Abstract
Based on the results of the election of candidates for President and Vice President of the Republic of Indonesia in 2019, an objection was submitted to the Constitutional Court over the results of the vote count by Candidate Pair 02 Prabowo-Sandi. Apart from questioning the correction of miscalculations, objections were also filed against various frauds that caused the candidate's defeat based on structured, systematic, and massive violations. In this article, the problems include: (1) How is the legal aspect of a structured offense, systematic and massive in disputes over election results? (2) What are the law enforcement regulations for election violations and disputes? (3) What is the interpretation of disqualification in the Constitutional Court Decision after the enactment of the Election Law and the Law on General Election of Regional Heads simultaneously? Data analysis used descriptive qualitative, by classifying, comparing, and linking legal materials with the subject matter. The conclusion is various reforms in law enforcement regulations for simultaneous election violations and disputes include the resolution of violations, criminal acts, and election disputes, including the resolution of structured, systematic, and massive violations, accompanied by strengthening of institutions and setting limits on the authority for settlement. In the 2019 Presidential and Vice-Presidential Election, the authority to enforce the law for measurable violations regarding the nomination requirements are submitted to the Election Supervisory Agency and disputed through the State Administrative Court. As for Structured, Systematic, and Massive violations, the reporting process is processed and decided by Bawaslu (Indonesian: Badan Pengawas Pemilu).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".