Konsep Disgorgement Fund Antara Indonesia, India dan Amerika Serikat Melalui Studi Perbandingan Hukum
Bibliographic record
Abstract
This research aims to analyze the concept of Disgorgement Fund in Indonesia, India, and the United States through a comparative legal study approach. The Disgorgement Fund is a legal mechanism aimed at returning illegally obtained profits to the aggrieved parties and serves as a form of recovery for investors. The methodology employed includes analysis of legislation, court practices, and regulatory policies in the three countries. The findings indicate that while all three countries share the same goals in the implementation of the Disgorgement Fund, there are significant differences in the implementation and legal approaches taken. In the United States, the Disgorgement Fund is detailed by the Securities and Exchange Commission (SEC), while in Indonesia and India, it is still in the developmental stage and often influenced by local social and economic contexts. This research provides recommendations for enhancing the legal framework and implementation of the Disgorgement Fund in Indonesia and India, as well as offering a broader perspective on understanding the importance of investor protection in the context of the global capital market.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".