Conceptual Relationships Between Country Image and Economic Security
Bibliographic record
Abstract
The paper deals with the analysis of the linking between country image and level of economic security. The studies confirmed that ongoing tendency on world competitiveness justified considering direct and indirect dimensions which influence on the country’s economic security. The authors generalised the factors which affected the economic security of the country. The finding proved that one of the significant indirect parameters is the country image. The authors analysed and summarised the most appropriate definitions of country brand and image. The authors analysed the frequency of country brand and image in the scientific paper. The paper aimed to identify the scientific clusters on the investigation of economic security and the country’s brand. The study used the bibliometric analysis using software VOSviewer, Web of Science and Scopus Tools Analysis. The data of analysis was obtained from the scientific database Scopus and Web of Science for 1991-2019 years. For the analysis, the papers were selected as the criteria as follows: English language; published for 1991-2019 years; contained the keywords brand, image, county and security. The results of the analysis proved that the paper which used the term “country image” is more than “country brand” in Scopus and Web of Science. The findings from the Web of Science and Scopus Tools Analysis confirm the hypothesis on the increasing tendency of publishing activities papers which focused on the analysis of security and country’s image. The visualising of co-authorship by country allowed identifying that the 50% papers published by the scientists with the American, Ukrainian, Canadian, Spanish and Russian affiliations. The VOSviewer results allowed identifying two significant scientific clusters as follows: country image and economic security. Besides, between the two most significant cluster were as mediators: governance, nation branding, developing countries and environment. Besides, analysing the new publication for 2015-2019 years, the most cited papers on economic security connected with analysis not only quantitively economic parameters but also with political, sustainable development and country’s image. The bibliometric analysis formed the background for checking the casual relationship between image and economic security of the country. Keywords bibliometric analysis, brand, brand management, country brand, VOSviewer.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".