Legal Protection for Assignee over Repeated Cession Based on Indonesia Legal System
Bibliographic record
Abstract
Humans require fundings in fulfilling their needs in life, such as primary, secondary, and tertiary necessities. Funds are used for some purposes such as venture development, working capital, investment, etc. In accordance to the function of bank which is to gather and distribute fundings to the society, banks may distribute such fundings in the form of loan. The granting of loan from banks as creditors is written in a loan agreement document. In fact, there will always be risk of non performing loan, which lead to the process of Cession, to shift the creditor’s right to claim debt payment, from Bank (as Assignor) to a new creditor (as Assignee). Repeated process in assigning right to claim receivables may cause loss to the Assignee, and the right of the Asignee has to be protected by the law. The method used in this study is the juridical normative method on a descriptive analytical nature. The study also uses statue approach and conceptual approach. The aims of this research is to have further review and analysis, about how Indonesian legal system regulates the settlement of credit, related with cession / assignment, which has been done more than once. The conclusion that can be drawn is: Cession is a legal action which causes a main legal consequences, that is shifted right to claim payment of debt, from first creditor to the new creditor. Debtor still have obligation to pay the debt, but now to the new creditor. In fact, cession is done because the first creditor consider several conditions in the debtor, that makes the debt potentially unpaid. The new creditor has to consider and understand the risks before signing cession agreement. Repeated cession has no clear regulation in Indonesia, but it’s commonly done by bankers and credit practicioners. This research sugests: government should issue regulation regarding the implementation of repeated cession, in order to protect the rights of the last Assignee. For bankers and credit practicioners, repeated cession should not be considered as recommended way to solve non-performing loans.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".