Mapping the Evolution of Sustainable Financial Inclusion: A Bibliometric Analysis of Global Trends (2007–2025)
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
Sustainable financial inclusion is an essential factor for economic development, social justice, and environmental sustainability. The primary objective of this bibliometric analysis is to investigate trends in sustainable financial inclusion publications using 1467 Scopus and WoS-indexed documents published between 2007 and 2025. The review visualized major trends, intellectual structures, and thematic clusters using VOSviewer and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol. This analysis identified eight thematic clusters, including digital finance, Environmental, Social, and Governance (ESG) integration, green finance, and financial literacy, which demonstrate the multidimensional nature of the field. Since 2017, research on sustainable financial inclusion has grown, led by China, India, and the USA, revealing geographic imbalances and underrepresentation of the Sub-Saharan Africa and Central Asia regions. Major barriers identified were financial illiteracy and uncoordinated regulations among institutions. This review suggests critical insights for scholars, policymakers, and practitioners should align inclusive finance with the Sustainable Development Goals (SDGs) and advocate for a shift from mere financial access to systemic, sustainability-driven models. It calls for collaboration between decision-makers and financial institutions to foster inclusive, fair, sustainable, and environmentally responsible financial ecosystems.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.019 | 0.073 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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