Moving Beyond Silo Thinking: A Deductive Analysis of Financial Literacy, Financial Inclusion, FinTech, and the UN Sustainable Development Goals
Bibliographic record
Abstract
Financial literacy, financial inclusion, FinTech, and the UN Sustainable Development Goals (SDGs) have thus far been scrutinized only in pairs or separately, without considering their interdependencies and impacts. This lack of examination calls for a deductive argumentative approach to comprehensively analyze all four aspects coherently. The objective is to establish a holistic framework for attaining the SDGs through financial literacy, financial inclusion, and FinTech. The argumentation reveals the existence of intricate theoretical and empirical links between all four objects of investigation. Previous silo thinking or bilateral approaches fall short of fully understanding the comprehensive effects. This paper adopts a holistic perspective, with financial literacy serving as the starting point, as it is indispensable for establishing a positive correlation between financial inclusion, FinTech, and the SDGs. Therefore, financial literacy fosters the adoption and utilization of FinTech, contributes to financial inclusion, and facilitates the achievement of the SDGs. The holistic framework can also guide policymakers in formulating recommendations. Decision-makers should adopt a comprehensive outlook encompassing all four points and prioritize the promotion and expansion of financial education.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".