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  • Что определяет миграционные намерения горожан: результаты опросов населения, проведенных в 10 регионах России в 2018-2023 гг.
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News
May 25, 2026
HSE Scientists Train Neural Network to 'Hear' Faults in Electric Motors
Researchers at the AI and Digital Science Institute of the HSE Faculty of Computer Science have developed a new method—the Signature-Guided Data Augmentation (SGDA) framework—that achieves 99% accuracy in motor fault detection and 86% accuracy in fault classification. The application of this approach can reduce industrial equipment repair costs, minimise downtime, and improve production safety. The study results have been published in Engineering Applications of Artificial Intelligence.
May 25, 2026
'The Humanities Serve as a Conscience'
Maria Mizernaia studies Soviet literature and the history of book publishing. In this interview for the HSE Young Scientists project, she discusses plans to publish a novel about besieged Leningrad, AI-provoked reflections on what it means to be human, and how novels can help satisfy our dopamine hunger.
May 25, 2026
Is It Possible to Predict a Citys Life Based on the Shape of Its Neighbourhoods?
Is it possible to predict, based on the configuration of streets and buildings, where a café will open or where traffic congestion will occur? Participants in the Spatial Analysis and Modelling of Urban Processes research and study group use open data and machine learning to identify universal patterns. Alexander Sheludkov and Eduard Somov discuss the purpose of comparing cities, the need for new forms of urban statistics, and how open data is transforming approaches to urban studies.

 

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Что определяет миграционные намерения горожан: результаты опросов населения, проведенных в 10 регионах России в 2018-2023 гг.

Демографическое обозрение. 2024. Т. 11. № 4. С. 136–167.
Balaban M., Rodina O., Chertenkov K., Sharepina E.

The purpose of the article is to analyze what socio-demographic characteristics and characteristics of the place of residence determine the presence or absence of migration intentions. The source of data was a survey database (6,823 questionnaires in total) collected by street survey method as part of expeditions in 10 regions of Russia in 2018-2023, devoted to the analysis of migration processes (republics of Udmurtia, Bashkortostan, Buryatia, Khakassia, Komi, Yamalo-Nenets Autonomous Okrug, Sakhalin, Voronezh, Saratov, and Tver oblasts). Descriptive statistics, correlation analysis, and logistic regression were used to analyze the factors of migration intentions. The odds of having migration intentions were higher for young people than for older people; for women than for men; without a partner/spouse; unemployed and economically inactive compared to those with employment and with higher education compared to those without. Individual differences in migration intentions do not change when controlling for settlement variables. Populations in cities with 50 to 100 thousand inhabitants are more likely to have migration intentions, as are localities with worse urban environment characteristics, located in the periphery, with higher relative average wages. Specialization in mining and manufacturing industries, depopulation of more than 10% since 1989, high share of non-local residents, wide offer of professional education programs, in contrast, reduce the chances of those living in such areas to have migration intentions.

Research target: Sociology (including Demography and Anthropology
Language: Russian
DOI
Text on another site
Keywords: внутренняя миграцияРоссияRussiainternal migrationmigration intentionsмиграционные намеренияфакторы миграцииstreet surveyуличный опрос
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