Evaluation on the Verification Implementation of Political Parties Participating in the 2019 General Election in Indonesia
Bibliographic record
Abstract
The judgement of the Constitutional Court (MK) Number 53/PUU-XV/2017 oblige all political parties participating in the 2019 general election both established parties or new ones to comply to the re-verification process. Political parties participating in the 2019 General Election must adhere to a verification starting by completing a Political Party Information System (SIPOL). The issue started when the General Election Supervisory Agency (Bawaslu) decided that the SIPOL is not the decisive factor to decide whether a political party passed or failed the administration screening, resulting in the General Election Committee (KPU) to issue a Decision Letter regarding Political Parties participating in the 2019 General Election after the decision of the Bawaslu RI. After the KPU also issued SK Number 58/PL.01.1/Kpt/03/KPU/II/2018 regarding Political Parties participating in the 2019 General Election provoked the political parties stated to fail to become participants in the General Election, to submit complaint through the administrative court. The problem became more entangled when parties winning the complaint in the administrative court reported the KPU commissioners stating to conduct efforts of a judicial review (PK). This paper intents by normative approach to provide an evaluation on the verification process of political parties participating in the 2019 General Election and provide input on the efforts to improve the political parties’ verification process in the future.
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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 | 0.004 | 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 | 0.000 | 0.000 |
| 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".