Implementation of the Constitutional Court Decision Regarding the Execution of Fiduciary Guarantees and Inclusion of Default Clauses in Indonesia
Bibliographic record
Abstract
The existence of the Constitutional Court Decision Number 18/PUU-XVII/2019, made problems in society related to the implementation of the fiduciary guarantee execution. This study aims to determine and analyze the implementation of the Constitutional Court Decision Number 18/PUU-XVII/2019. The research method used is normative juridical by conducting document studies of legal principles, legal regulations and legal norms in Indonesia and interviews with civil law experts. The results showed that the decision of the Constitutional Court Number 18/PUU-XVII/2019, caused disagreements in its implementation. Prior to the Constitutional Court Decision, the execution of the Fiduciary Guarantee was based on the Fiduciary Guarantee Law, if the debtor in default, the Fiduciary Recipient can execute on the basis of the fiduciary recipient's own power to sell the object of fiduciary security, but with a Constitutional Court Decision it must go through a court. This creates confusion for creditors and is against the principle of material security. This is detrimental to creditors, because creditors cannot immediately sell their own fiduciary collateral objects if the debtor defaults. This phenomenon can lead to a lack of legal certainty and legal protection for fiduciary recipients and contradicts the nature of fiduciary guarantees which should have strong guarantee rights and are easy to implement.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".