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September 25, 2026
AI Users Earn Up to 41.8% More Than Non-Users
Research conducted by economists at HSE University has revealed a significant correlation between the regular use of GenAI in the workplace and higher pay among Russian employees. The study found that individuals who frequently use GenAI in their professional activities earn notably more than those who reject these new tools or resort to them occasionally. The salary premium for highly qualified specialists reaches 41.8%. The article was published in the Voprosy Ekonomiki journal.
September 24, 2026
‘Feedback and Constructive Criticism Are Essential in Our Profession
Vincent Fardeau, Associate Professor at HSE ICEF, has reached a major career milestone: he recently published his paper ‘Asymmetric Thin Markets’ in the Journal of Financial Economics, successfully passed his major academic review, and received tenure. In this interview, Vincent discusses the story behind the paper, explains the concept of asymmetric thin markets, and shares his advice for young scholars aiming to publish in top-tier journals.
September 22, 2026
Personal Interest in Doctoral Thesis Topic Most Important for Confidence in Successful Defence
A researcher at HSE University analysed data on 1,539 doctoral students from 161 Russian universities to identify which features of a thesis topic are associated with academic success and engagement. The most important factor was found to be personal interest in the research topic, which was associated with almost all key aspects of doctoral programme experience—from engaging with the academic supervisor to research activity and confidence about successfully defending the thesis. The findings have been published in Higher Education.

 

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Роли локальной идентичности в сопротивлении реновации: кейс района Свиблово в Москве

Городские исследования и практики. 2023. Т. 7. № 4. С. 68–86.
Pereyaslavtsev D.

This paper explores the effects place image has on the place identity of residents of Sviblovo district in Moscow protesting the renovation program. Sviblovo was chosen as an example because of the abundant protests against Moscow’s renovation project from 2017 to this day. The main methodology used is the semantic analysis of social network and petition texts using NLP algorithms. The patterns observed are then supported with mass media and other research material. The results show a correlation between urban conflicts and place image mobilization in the texts and everyday speech of protesters. For example, a place is more frequently described in terms of its natural or constructed features, when the same group of images were used to defend the place in urban protest activities. The patterns can also be seen in a retrospective analysis of the everyday communication of Sviblovo residents and other urban conflicts that took place in the district. Further research which includes a larger sample of districts is needed in order to confirm the findings.

Language: Russian
DOI
Text on another site
Keywords: локальная идентичностьобработка естественного языкаобраз местагородские конфликтыplace identityNatural Language Processing (NLP)place imageurban conflicts
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