Migration of scientists: digital footprint and scientometry
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,006 | 0,033 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,016 | 0,095 |
| Études des sciences et des technologies | 0,000 | 0,003 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».