Bibliometric analysis of scientific research on trust in the financial sector of the economy
Why this work is in the frame
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Bibliographic record
Abstract
Introduction. Trust in the financial sector of the economy affects the coherence of the financial system and macroeconomic stability. In this regard, it is essential to provide a thorough methodological basis that will be the basis for studying this concept. The multifaceted nature of the researched concept presupposes multilevel bibliometric analysis based on analytical tools ScopusTools, Google Trends, and VOSviewer.Prupose. The study aims to conduct a multilevel bibliometric analysis of research on trust in the financial sector.Methods. The methodological basis of the study is a set of scientific publications indexed in the scientometric database Scopus for 1863-2021. To achieve this goal, the following general scientific research methods were used: analysis of scientific literature, theoretical synthesis, grouping, sampling method, contextual content analysis, evolutionary-temporal and spatio-temporal analysis, comparative and cluster analysis.Results. The obtained results testify to the growing tendency of scientific research on trust in the financial sector. Six stages of scientific interest have been identified, so the stage of active development of research falls on 2008-2012. and 2015-2018. The geographical centers of research are the United Kingdom, the United States, and Canada; these countries began to explore the concept of trust in the financial sector earlier than other countries. Analysis of the sectoral structure of research on trust in the financial sector demonstrates the interdisciplinary nature of the phenomenon under the study. A comparative analysis of Google’s search queries shows that one of the key conditions for overcoming macroeconomic imbalances can be considered trust in the financial sector. Cluster analysis identified five main research clusters.Discussion. The prospect of further research is to apply the results of the bibliometric analysis to form a clear structure of factors of trust in the economy’s financial sector and develop a scenario of action due to its violation.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.037 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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