Social Stock Exchange Funding Dynamics: Navigating Factors for Social Enterprises and Sustainability Projects
Bibliographic record
Abstract
Social Stock Exchanges (SSEs) are a novel development for creating social investment platforms for social enterprises (SEs) or sustainability projects (SPs) to raise capital from impact investors. In this article, we study the influencing factors driving higher funding rates for SEs/SPs on SSE platforms in the UK, Canada, Singapore, the USA and Jamaica, using panel fixed effects stepwise regression models, controlling for time, sector and country-year fixed effects. Our robust empirical findings show that both equity and debt financing tools utilized by SEs/SPs and women population as target beneficiaries have a positive and significant relationship with a higher funding rate when controlling for time and sector fixed effects. We also find that collaboration amongst SEs/SPs, as well as different kinds of firm ownership (non-profit, for-profit and cooperatives), has a significant positive effect on gaining a higher funding rate, when controlling for both time and sector fixed effects, as well as time, sector and country-year fixed effects. Overall, our article enlightens about the driving factors for building diverse SSE platforms globally.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.007 | 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".