ANALYSIS OF FINANCIAL SECURITY RESEARCH VECTORS: BIBLIOMETRIC ANALYSIS AND VISUALIZATION
Bibliographic record
Abstract
Introduction. Bibliometric analysis allows the identification of major trends and priority research areas, as well as the identification of reputable scientists and their contribution to the field. Using the software package VOSviewer v.1.6.14, we analysed the common use of keywords in publications and the identification of partner countries, selecting the two most popular scientific databases Scopus and Web of Science as search tools. The purpose of the article is to identify trends and vectors of scientific research on financial security in order to identify the main directions in related fields. The bibliometric analysis of publications related to finance, economics and security made it possible to identify areas of research in this field and to formulate a quality policy aimed at strengthening national security and improving economic regulation. Results. With the development of technology and the globalisation of the information environment, the number of studies on the topic has increased. The number of relevant research indicators was taken from the Scopus database - 1970 literature, WoS - 1936.. Financial security is studied in several disciplines. According to the Scopus database, the most relevant fields are social sciences, economics, econometrics and finance. According to WoS, financial security is studied within the field of business and finance. Results are also provided for the most cited publications. Researchers from the USA, UK, China, Canada, Australia, Ukraine and Germany have contributed most to the development of this field. The results of the bibliometric analysis showed that there were 8 data clusters from the Scopus database and 11 data clusters from the WoS database. Conclusions. The study revealed the growing interest of the scientific community in the analysis of financial security issues. The direction of scientific research leads to the conclusion that financial security affects all areas of the economic environment and quality of life. A bibliometric analysis of publications by keywords shows that the concept of financial security is often associated with other concepts. This shows that the concept often coincides with categories such as economy, security, environment, etc. The results obtained allow us to determine the vector of focus for further assessment of the country’s financial security.
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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 | 0.008 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.099 | 0.113 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".