Social Stock Exchange Funding Dynamics: Navigating Factors for Social Enterprises and Sustainability Projects
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it