What's in a name? Exploring the intellectual structure of social finance
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
Purpose This paper offers a bibliometric analysis of the scientific literature on social finance. It provides an overview of the research field by identifying gaps in the existing academic literature and presenting future research directions. Design/methodology/approach The study uses co-word analysis and visualization mapping techniques. Findings This study's findings show that the social finance research field comprises five main research clusters and four main research hotspots—impact investing, social entrepreneurship, social impact bonds, and social innovation—which represent the core of this research domain. The authors also identify the researchers and the research institutions that have contributed to the development of social finance. In addition, emerging research areas are mapped and discussed. Originality/value Compared with most previous literature reviews, this work provides a more complete and objective analysis of the entire social finance landscape by revealing the trends and evolving dynamics that characterize its development. To this end, clear terminological boundaries have not yet been established in social finance. The field appears immature because only a few researchers have contributed to it, and papers have yet to be published by top finance journals. Finally, the findings of this research provide directions for future studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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