Bibliographic record
Abstract
Migration, its quantitative and qualitative changes is one of the topical issues. First, scientists are direct participants in scientific and technological progress, the level and development of which is a priority in most countries, countries compete to attract the best "minds" of the world, and scientists in search of decent conditions for their activities. Second, it is difficult to assess the migration of individual professional circles. One of the tools for analyzing and evaluating the migration of scientists is the analysis of their profiles, which became possible thanks to the digitalization of scientific activities (development of scientometric databases: SCOPUS, WoS, eLibrary, PubMed, professional networks-research gate, assignment of identification numbers of scientists – ORCID and other tools), so-called digital traces of scientists remain in the network (their research, full name, affiliation to a scientific organization and their change, sphere of interests and other indicators), as a result, it becomes possible to evaluate the interaction of scientists, analyze mobility, evaluate quantitative and qualitative indicators of migration. At the same time, the analysis of scientists by their identification indicators is called scientometry, or in the practice of domestic terminology bibliometry. The popularity of bibliometric analysis begins with the 1980s with a sharp increase in the number of articles using this method to 2019, However, the origins of the method go in the 20-ies of the XIX century Using bibliometric data to assess the quantitative indicators of academic and study (however, put forward hypotheses about ambiguous citation of the article and its significance to the scientific community), to perform the migration and its qualitative and quantitative changes. As part of the research work, the staff of the Laboratory for University development of UrFU developed an algorithm for generating data from the scientometric database SCOPUS. The purpose of the algorithm is to analyze and evaluate the migration of scientists. The developed algorithm generates data as follows: j-th number of rows, which represent the full name of the authors and their ID, i-th number of columns, including scientometric data (scientific field; country; University; total number of articles, and others). The profiles in which the affinity has changed are important for further research. The resulting database was "cleared". The largest outflow of scientists from UrFU, as well as throughout Russia, occurred in the 90s, as well as 2000-2002. among the leaders of the host countries were the United States, Israel, England, Canada, Germany, France, Czech Republic, post-Soviet space-Belarus, Ukraine, Moldova, Uzbekistan. In General, the Ural Federal University is characterized by positive dynamics, which is represented, on the one hand, by a decrease in the share of migrating scientists, on the other hand, by the involvement of foreign specialists for the implementation of joint projects. Among the main conclusions, it is worth noting that there is a change in the nature of migration: its transition from brain drain to drain sharing, i.e. from irrevocable migration and loss of intellectual capital to its sharing. The digitalization of scientometry is a significant advance that contributes not only to the dissemination of scientific knowledge, but also to its protection (for example, the detection of plagiarism), and digital traces are an important tool in the analysis of quantitative and qualitative scientific indicators. © 2020 LLC Ecological Help. All rights reserved.
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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 | 0.006 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.016 | 0.095 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".