Social Stock Exchange: The Way Forward for India
Bibliographic record
Abstract
Social entrepreneurs have the potential to make a significant impact on the socio-economic development of the nation, but they struggle to find sustained funding. Social Stock Exchange can aid such for-profit social enterprises by bridging the funding gap and creating an investment ecosystem. A Social Stock Exchange (SSE) is a regulated platform to bring together social organizations to raise capital and impact investors to make financial returns while ensuring social and environmental sustainability at large. Globally, SSEs are already instituted in a handful of countries including Canada, Singapore, South Africa and the United Kingdom. In India, the vision of SSE was first floated by Union Finance Minister Nirmala Sitharaman in her budget speech in July 2019 to achieve various social welfare objectives related to inclusive growth and financial inclusion. Thereafter, in September 2019, the Securities Exchange Board of India (SEBI) constituted a working group to propose a feasible architecture for setting up the SSE mechanism. On September 28, 2021, SEBI approved the creation of the SSE under its regulatory ambit, providing a renewed impetus to social entrepreneurs and impact investors in the country. There is a dearth of research on SSEs in the public domain; therefore, this chapter attempts to do exploratory research striving to evaluate India’s framework of SSE approved by SEBI in contrast to the global models of SSEs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.029 | 0.011 |
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".