New insights on social finance research in the sustainable development context
Bibliographic record
Abstract
Abstract Research on sustainable finance has experienced significant growth in recent years, but the exploration from a comprehensive perspective is still in its nascent stages. As of July 2023, our research revealed that this area remains relatively underexplored in the existing body of knowledge, leading to a notable lack of comprehensive research analyzing the current state‐of‐the‐art in the social finance arena. To address this gap, our study takes a pioneering approach by utilizing scientometrics and network analysis techniques, specifically employing VOSviewer and Bibliometrix in conjunction with Web of Science and Scopus databases. By merging data from both sources and removing duplicate entries, we established a consolidated database of 401 relevant studies. Through our analysis, we have identified prominent authors, sources, and the most influential studies in the social finance arena. Additionally, we examined the coupling of studies and authors to ascertain their significance in this emerging domain. The results have unveiled several prominent further research, including mainly social banking, Islamic finance, social innovation, the impact of the COVID‐19 pandemic, impact investing, social impact bonds, and Sustainable Development Goals. By shedding light on the current landscape, our findings comprehensively understand the field's progress and potential directions. This insight is valuable for market participants, researchers, policymakers, and decision‐makers seeking to navigate and contribute to the evolving landscape of sustainable finance with a social focus. Furthermore, our innovative use of scientometrics and network analysis sets a precedent for future research exploring the complex interplay between finance, development, and sustainability.
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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 | 0.012 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.033 | 0.038 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.016 | 0.018 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".