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News
June 2, 2026
HSE Study Reveals Imbalance in the Generative AI Market
Researchers at HSE University analysed how effectively the global generative artificial intelligence market converts investment into real revenue, concluding that AI is currently developing faster than it is paying off. The results have been published in the journal Foresight and STI Governance.
June 2, 2026
Discovering Science through Russian Language: HSE Prep Year Students Present at International Conference in Kazan
On May 23, 2026, the V International Scientific and Practical Conference ‘Discovering the World of Science’ took place in Kazan at the Preparatory Faculty for International Students of Kazan Federal University. Four students of the HSE International Preparatory Year took part in the event: two delivered their presentations in person, while two participated online. Their work was supervised by Acting Director of the International Prep Year Irina Isaeva and lecturer Ekaterina Kozhemyakova.
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.

 

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Факторы мобильности на рынке труда в современной России: имеют ли значение социальные связи?

Экономическая социология. 2026. Т. 27. № 1. С. 43–78.
Халиков К.

The aim of this study is to assess the impact of various factors, including social networks, on labor market mobility in modern Russia. The main assumption is that weak ties facilitate taking a better job. The empirical base consists of  data from the Russian Longitudinal Monitoring Survey (RLMS) of HSE for 2016–2017 and 2018–2019. Based on exploratory factor analysis, indicators of different types of social ties were constructed: weak ties include ties with acquaintances and colleagues; with organizations; online networks; and strong ties include ties with friends and relatives. Binary logistic regression estimated the chances of the unemployed going to work, while multinomial logistic regression assessed the chances of employed people for vertical and horizontal interfirm and intrafirm mobility, as well as transitions to unemployment or inactivity. Men, people with higher education, and lower specific work experience are more inclined to labor mobility. The best opportunities for intra-firm growth are among those employed by state enterprises and those holding a higher position. The study found a significant positive effect of increased education and improved health on mobility, a negative effect of health deterioration, and a higher propensity for mobility among those dissatisfied with their current job. Granovetter’s assumption about a greater influence of weak social ties on the ability to get a good job could not be confirmed, although due to the instability of the results, it cannot be refuted either. The model estimates showed a stable and equally strong effect of both strong (with friends) and weak (with acquaintances, former colleagues, neighbors) ties on the chances of finding a job for the unemployed, as well as a negative effect of online social networks on the chances of becoming inactive for the employed. No effect of social connections on intra-firm growth was found, and the effect of online networks on inter-firm promotion was inconsistent.

Research target: Economics and Management Sociology (including Demography and Anthropology
Language: Russian
DOI
Keywords: социальный капиталсоциальные сетипоиск работыsocial capitalsocial networksjob searchмобильность на рынке трудасильные/слабые социальные связиlabor market mobilityweak social tiesstrong social ties
Publication based on the results of:
Monitoring the socio-economic conditions of households in Russia, 1994-2026 (2027)
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