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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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Национальный корпус русского языка 2.0: новые возможности и перспективы развития

Вопросы языкознания. 2024. № 2. С. 7–34.
Савчук С. О., Архангельский Т. А., Bonch-Osmolovskaya A. A., Донина О. В., Кузнецова Ю. Н., Lyashevskaya O., Orekhov B., Подрядчикова М. В.

The paper provides an overview of the results of the fundamental reconstruction and modernization project of the National Corpus of the Russian Language platform, carried out from 2020 to 2023. The focus of the paper is on the new opportunities that are opening up for linguists and a wider audience. This includes improving the representativeness of existing corpora, creating new corpora, new annotation obtained through the application of neural network models, and new interface solutions. Three notable new components are examined in more detail: a resource-related one, which is the new Social Networks corpus, a search-related one, which is the Panchronic corpus that combines searches across corpora from different periods, and an analytical one, which is the functional complex of statistics and data visualization.

Research target: Philology and Linguistics
Language: Russian
Full text
DOI
Keywords: русский языккорпусная лингвистикаRussian languageНКРЯкорпусдиахронияcorpus linguisticsнейросетевое моделированиеdiachronyneural network modelinglanguage corporaквантитативная лингвистикаquantitative linguisticsThe Russian National Corpus Language of social networksязык социальных сетей
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