The Indian Securities Fraud Class Action: Is Class Arbitration the Answer?
Bibliographic record
Abstract
Abstract:\nIn 2013, India enacted one of the most robust private enforcement regimes for securities fraud violations in the world. Unlike in most other countries, Indian shareholders can now initiate securities fraud lawsuits on their own, represent all other defrauded shareholders unless those shareholders affirmatively opt out, and collect money damages for the entire class. The only thing missing is a better financing mechanism: unlike the United States, Canada, and Australia, India does not permit contingency fees, so class action lawyers cannot front the costs of litigation in exchange for collecting a percentage of what they recover. On the other hand, the 2013 law enacted a public financing regime for securities fraud class actions and it is possible third-party financing will be permitted; these mechanisms may make up some of the loss in effectiveness caused by the lack of contingency fees. It is still too early to tell.\nYet, commentators are very pessimistic that the Indian securities fraud class action will do much good because the Indian court system is glacially slow. For example, it takes over six years on average to resolve some civil appeals.\nThe solution to this problem in the 2013 law was to channel the securities fraud class action to a special tribunal, the National Company Litigation Tribunal (“NCLT”). Yet, this type of solution has been tried before in India: special tribunals tend to quickly take on the negative characteristics of the general courts. This may be why very few securities fraud lawsuits have been filed since the 2013 law was enacted.\nWe propose a different solution to the problem of the Indian court system: class arbitration. As we explain, although class arbitration is not perfect, it may better facilitate robust private enforcement than the Indian court system.
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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 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.010 | 0.015 |
| Scholarly communication | 0.020 | 0.011 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.020 | 0.032 |
| Insufficient payload (model declined to judge) | 0.022 | 0.004 |
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".