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News
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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Идентификация автора текста в сетевой коммуникации

Вестник Волгоградского государственного университета. Серия 2: Языкознание. 2022. Т. 21. № 3. С. 143–157.
Romanova T. V., Khomenko A.

The article deals with general and particular patterns of solving the identification problem of attributional linguistics on the texts of written network communication. The problem of determining text authorship is solved by comparing a disputed text, the author of which, according to the conditions of the experiment, is unknown, with texts, the authors of which are known. The problem is solved for several discursive areas: online literature, corporate e-mail, short comments on posts on the entertainment portal. These discursive spheres have correlates with several functional styles of speech, fiction, official and colloquial, which makes it possible to identify the trends in textual attribution within the material of Internet communication. For the analysis of speech material, a holistic approach, the tool of which is linguistic modelling, is used. The choice of parameters describing the author’s individual style is rooted to considering text to be a product of an authentic language personality. Language personality is described using psycholinguistic (Yu.N. Karaulov), sociolinguistic and forensic linguistic (S.M. Vul, M. Coulthard, R. W. Shuy) methods. With the help of specialized software "KhoRom", researchers create mathematical models of the individual styles of the compared texts authors. As output the resource displays Pearson correlation coefficient, linear regression and Student’s criterion for the models of the two idiolects represented in compared texts, as well as each parameter metrics values. The software is only a part of the attribution algorithm. The obtained mathematical statistics should be analyzed by experts with the usage of methodological recommendations developed for the algorithm. On the basis of these models and their study, a number of conclusions about statistical patterns of an authorship study in various discursive spheres of electronic communication are made.

Research target: Philology and Linguistics
Language: Russian
Full text
DOI
Keywords: математическая модельязыковая личностьmathematical modelingauthorship attributionлингвистическая модельlinguistic modelсетевая коммуникацияатрибуция текстаlanguage personalitynetwork communication
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