A Negative Pledge as an Alternative Solution to Achieve the Pari Passu Pro-Rata Parte Principle
Bibliographic record
Abstract
The relationship between creditors and debtors is unique. Creditors need debtors as customers and bind them in credit agreements. Creditors tend to be suspicious of debtors as debtors selectively disclose information. This relationship follows the agency theory. Creditors always want a fair and equal position with other creditors. This research is unique in discussing the situation between creditors in the concept of negative pledge and pari passu pro-rata parte. Aside from that, the study also observes the relationship between creditors and debtors in credit agreements and discusses solutions that can be given by debtors to creditors so that the pari passu pro-rata parte principle can be achieved. The pari passu pro-rata parte principle is regulated in the Indonesian Civil Code article 1131 - 1132 and Law on Bankruptcy article 176 jo. 189. The methodology used is the normative juridical method, specifically hermeneutics and idiographic from the economic and financial perspective. The research concluded that debtors and creditors could ensure a fair and equal position by implementing negative pledge through Master Credit Agreement and Security Sharing Agreement. Future research should study on the role of curators and judges as key people to keep the concept of negative pledge running well.
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 | 0.014 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.023 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".