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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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Труд водителей такси в условиях алгоритмического управления

Мониторинг общественного мнения: Экономические и социальные перемены. 2021. № 3. С. 356–381.
Shevchuk A., Чижова Д. А., Демина Д. Е., Чугункова В. А.

The study focuses on a new type of technological control, i.e., algorithmic control, widely used in decentralized organizational structures (digital platforms) aimed at meeting mass demand. On the example of the taxi sector, the author reveals algorithmic management mechanisms and problematizes its nature and impact on the work and well-being of platform workers. The empirical base of the study consists of 26 semi-structured interviews with drivers working in Moscow through taxi aggregators. The study identifies two forms of algorithmic control, directive and manipulative. Although in many key aspects algorithmic control has a rigid coercive nature (distribution of orders, price determination, electronic monitoring, deactivation), in general it is more likely aimed not at the total programming of human behavior, but at creating an open dynamic system of incentives and sanctions, supplemented by elements of gamification.

According to the research results, drivers appreciate many of the opportunities offered by platform employment: easy access to work, lack of formal hierarchy, flexibility in managing labor efforts along with time and finances. This allows customizing work for oneself, focusing on individual preferences and life circumstances, supporting a heterogeneous composition of platform workers with adverse social background, motivations, and work practices. The atypical nature of employment, the complex and opaque operation of the algorithms give rise to uncertainty about labor status of drivers and «rules of the game», and the situation of permanent assessment by clients creates psychological stress. Within the alienated technical system, drivers feel a lack of viable interpersonal contact, both professionally and emotionally.

Research target: Sociology (including Demography and Anthropology
Language: Russian
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DOI
Text on another site
Keywords: рынок трудаlabour marketнестандартная занятостьсоциология трудаорганизация трудаnon-standard employmentsociology of workorganization of worknon-regular employmentсоциология труда, профессий и занятийsociology of work, professions and occupationsцифровые платформыуберизациязанятость и рынок трудаlabor marketuberizationплатформенная занятостьplatform workтрудовые платформыdigital labour platformsалгоритмическое управление
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