A critical comparative analysis of the emerging and maturing regulatory frameworks: Crowdfunding in India, USA, UK
Bibliographic record
Abstract
This research paper undertakes a critical comparative analysis of the emerging regulatory framework relating to Crowdfunding and P2P Lending in India with the corresponding rules and regulations prevailing in the USA and UK in order to ascertain whether the draft regulatory framework in India is capable of extracting experiences and lessons from already enacted and mature regulations. We find that in framing the draft legislation in India, the regulators have obtained valuable guidance from a number of evolved legislations inter alia including USA, UK, France, Canada and Japan. The draft legislation proposed in India is innovative in many ways as the securities and banking regulators have attempted to adapt the various rules and regulations to the existing institutional infrastructure in India. However, in doing so, at times, the legislator forgets that at the early stages of an industry, regulation should be enabling and not limiting.JEL Codes: K22, D14, G2, L86, M38, O16
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".