New insights on social finance research in the sustainable development context
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
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it