Legal Politics of Party Simplification in Indonesia: A Study Based on the Political Party Regulatory Model
Bibliographic record
Abstract
Although the idea of simplifying the party system is a noble endeavour, the choice of legal politics for simplifying political parties must be democratic and adhere to the model of structuring political parties with the right regulatory model. Therefore, this paper aims to identify the nature of political law and regulatory models for the simplification of the party system applied in Indonesia. This research is normative legal research which focuses on the study of statutory regulations and doctrines, while the research is descriptive-exploratory with statutory and conceptual approaches. The data used are secondary data consisting of primary and secondary legal materials. The data obtained were processed and analyzed qualitatively, comprehensively, and completely. Based on the identification results, the idea of a political direction for simplifying the party system in Indonesia began in the general election period of 2004, 2009, and 2014 through changes to the law on political parties and general elections. But unfortunately, the simplification of the party system and the law has not been able to be directed at a simple party system consisting of 3-5 political parties, besides that the simplification of the party system has also not been focused on simplifying the party system participating in the General election. Every shift in legal politics from the simplification of the party system during the general election shows the democratic nature of legal politics and adopts a simplified legal model from the party system prescription model, the licensing model, the promotion model, and the protection model.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".