What's in a name? Exploring the intellectual structure of social finance
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.
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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 | 0.007 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.051 | 0.065 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".