BIBLIOMETRIC ANALYSIS OF PUBLICATIONS ON MIGRATION, ECONOMY AND SECURITY OF THE COUNTRY
Bibliographic record
Abstract
The paper presents a bibliometric analysis of publications on migration, economy and security of the country. The visualization method was used to visualize the results of the study. The study was conducted in VOSviewer. Publications from the scientometric database Scopus for the period 1645-2020 were taken for analysis. The search query was formed from the following keywords: "migration", "human mobility", "econom *", "security", "safety". In addition, for a more accurate search, the query was limited to the following areas of knowledge: Social Sciences, Environmental Sciences, Agricultural and Biological Sciences, Economics, Econometrics and Finance, Arts and Humanities, Business, Management and Accounting, Multidisciplinary, Decision Sciences. A total of 1,781 documents were processed, of which 1,192 were scientific articles. The article analyzed the sharing of keywords in publications using VOSviewer. Analysis of scientists' publication activity has shown that there is a growing interest in studying issues related to the relationship between migration, economy, and security in the scientific community. The largest number of publications on the researched issue during the analyzed period was recorded in 2020 and amounts to 179 documents. The publications' geography showed that scientists made the most significant contribution to the development of research on this issue from the United States, Great Britain, Canada, Germany, Australia and China. An analysis of scientific cooperation on the research topic showed that the United States and the United Kingdom have the largest number of relationships with other countries to conduct joint research in this area. Keyword clustering has made it possible to identify four clusters, including words grouped by the most common areas of research. The most popular areas are research on the impact of climate change on migration, the relationship of migration processes with a socio-economic change in countries, the connection between migration and national security, etc.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.012 | 0.081 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".