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News
August 13, 2026
‘Working with AI Solves a Wide Range of Engineering Problems
Artificial intelligence is a working tool based on a balanced combination of algorithms and engineering. Experts and doctoral students from the HSE Moscow Institute of Electronics and Mathematics explain how AI technologies can improve an application, device, or system, and what engineering tasks are solved in the process.
August 12, 2026
‘I Would Like My Research to Help Make the World a Calmer and Better Place
Whatever task Saraa Ali, Junior Research Fellow at the Laboratory of Methods for Big Data Analysis (LAMBDA) of the AI and Digital Science Institute (HSE Faculty of Computer Science), is working on, she thinks about how it can benefit people. She told the Young Scientists of HSE University project about her large family, diagnosing three-phase motors, and her dream of building a children’s home in her native country.
August 11, 2026
‘The Peak of Stupidity and ‘The Valley of Despair: HSE Economists Propose an Explanation for the Dunning–Kruger Effect
The Dunning–Kruger effect, which describes a sharp surge in self-confidence among beginners followed by an equally rapid decline as they gain experience, can be explained by the nature of the learning process and the acquisition of new knowledge. This conclusion was reached by Andrey Vorchik of the HSE Faculty of Economic Sciences together with independent researcher Murat Mamyshev. They developed a mathematical model of learning and demonstrated how subjective confidence is formed and changes as knowledge accumulates, as well as how teachers can reduce the ‘valley of despair’ experienced by learners.

 

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Факторы научной продуктивности и проблемы ее оценки

Университетское управление: практика и анализ. 2018. № 1. С. 61–75.
Litvinova L.

The article is a review of a research into academic productivity factors and approaches to its evaluation. The search for mechanisms of evaluating and managing academic staff productivity is an important challenge for research administrators and academic funds executives. Existing publications on academic productivity cover mainly two aspects: 1) methodology of productivity measurement and 2) factors negatively and positively influencing productivity of academic staff. This article presents main ideas of bibliometric approach to productivity measurement, different productivity indicators are classified and discussed. Factors influencing productivity are subdivided into three levels: individual (age, cooperation with colleagues, interdisciplinarity, internationalization, mobility and inbreeding, educational background and initial professional experience; marital status, gender); organizational (financing, organizational culture, bonus system, access to resources, prestige of an organization) and institutional (level of economic development of the country, political factors, religion). The article discusses an opportunity of considering different factors in administrative policy.

Priority areas: economics
Language: Russian
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Keywords: академическая профессиябиблиометрияacademic professionнаучная продуктивностьbibliometricsscientometric indicatorsнаукометрические показателиacademic productivityperformance of scholarsadministrative policy in universityэффективность научных сотрудниковадминистративная политика в университете
Publication based on the results of:
The Influence of Institutional Peculiarities of the Russian Higher Education Market on the Strategies of Professors and Students (2017)
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