The Proportionality Test Models of Competing Rights Cases in the Civil and Common Law Systems: Lesson to Learn for Indonesia
Bibliographic record
Abstract
This research focuses on the Proportionality test model of Competing Rights in practice in civil law countries (Germany and South Korea) and the Common Law System (United States and Canada). The research method used is a normative legal research method with statutory, comparative, and case approaches. The results show that the proportionality test is the "ultimate rule of law," a fundamental benchmark in judicial review, and has become a global constitutionalism recognised and applied internationally. Its application is structured and systematic with four test stages, such as German, Canadian, and South Korean models. Meanwhile, it is unstructured in the United States, and there is only one analytical tool (balancing test). In the case of decision, the four stages are only sometimes applied, but according to the needs of the analysis. If, at the third stage (necessity/minimal impairment), it is found that the object being tested is contrary to the Constitution, then the argumentation focuses on that analysis of it. The fourth stage is used if the case is more complicated and requires analysing the balance of norms and legal values. Meanwhile, in the Indonesian Constitutional Court practice, there is a proportional analysis, but it is partial, unstructured, and unsystematic. Therefore, in the future, it is essential to develop an Indonesian model of the principle of proportionality under the values of Pancasila and the 1945 Constitution.
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.006 | 0.013 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| 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".