61 research outputs found

    Π“ΠΎΠ΄ послС Π²ΡΠΏΡ‹ΡˆΠΊΠΈ COVID-19: восприятиС ΠΏΠΎΡ‚Π΅Π½Ρ†ΠΈΠ°Π»ΡŒΠ½Ρ‹ΠΌΠΈ студСнтами качСства Π²Ρ‹ΡΡˆΠ΅Π³ΠΎ образования Π² контСкстС Ρ†ΠΈΡ„Ρ€ΠΎΠ²ΠΈΠ·Π°Ρ†ΠΈΠΈ ΠΈ смСшанного обучСния

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    Introduction. The forced transition of Russian universities to distance learning in 2020 and accelerated digital transformation of educational processes in higher education are the first effects of the COVID-19 pandemic. A key aspect of measuring higher education quality is the perception of its formats by students as university change agents. The aim of the study is to identify the factors that determine the applicants’ positive attitude to learning that includes online elements in the context of the Russian universities’ transition to the blended learning model. Materials and Methods. The empirical base of the research includes the results of an online sociological survey conducted among the applicants for Ural Federal University undergraduate and graduate programmes in 2021. The methods of classification, factor analysis, and coefficients of pair correlations were applied. Additionally, for comparison, data from 2015 for a similar sample (1st year bachelor’s degree students) were used. Results. Positive attitudes towards online and blended learning are gradually increasing. The factor analysis of data from 2021 showed that applicants who support the online and blended learning include: those aspiring for master’s degree upon completing their bachelor’s degree course; those who choose their degree field rationally – men who apply for a state-funded education in any Russian university (including participants of federal contests – β€˜Academic Olympics’). The above groups are formed mainly under the influence of external factors. Another group includes those oriented towards self-realization – women who choose their degree field relying on their personal inclinations for a future profession (the influence of internal factors). Discussion and Conclusion. The research results contribute to the development of scientific ideas about the blended learning model and emphasize the value of institutional research based on feedback from university students for making informed management decisions on change. The materials of the paper will be useful when designing the educational process in the Russian universities’ transition to the blended learning model. Β© 2021 National Research Ogarev Mordovia State University. All rights reserved.Funding: The work was supported by Act211 Government of the Russian Federation, contract No. 02.A03.21.0006

    Optimization of Students' Graduation by the University Taking into Account the Needs of the Labor Market

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    The development of the socio-economic system and the labor market is directly related to the training of young specialists in higher education institutions in accordance with the needs of developing regions. To optimize the functioning of the labor market, it is necessary to compensate for the shortage of highly qualified personnel depending on the areas of training and determine the structural proportions of the optimal number of graduates, based on the share of employed and unemployed in various sectors of the economy. The University meets the needs of regional labor markets with a significant proportion of young highly qualified specialists. To optimize the educational process, it is necessary to analyze and model the impact of educational paths of graduates on the labor market by determining the equilibrium unemployment in the labor market. The proposed approach combines a model for maximizing the expected salary of students with a modification of the search and matching model. At the first level of model construction, we apply an econometric model that allows us to adapt educational paths to the interests of students. At the second level, we describe the behavior of students, choosing an educational path. At the third level, the structure of graduates adapts to the requirements of the labor market. The research perspective is the introduction of feedback mechanisms from graduates of regional Universities using surveys for a comprehensive assessment of the quality of graduate programs of the University with administrative data on the educational paths of graduates. Β© 2020 Elsevier B.V. All rights reserved.The reported study was funded by RFBR according to the research project No 18-311-00175

    УнивСрситСтский ΠΈ Ρ€Π΅Π³ΠΈΠΎΠ½Π°Π»ΡŒΠ½Ρ‹ΠΉ Π»Π°Π½Π΄ΡˆΠ°Ρ„Ρ‚ российской аспирантуры, финансовыС Ρ‚Ρ€Π°Π΅ΠΊΡ‚ΠΎΡ€ΠΈΠΈ ΠΎΠ±ΡƒΡ‡Π°ΡŽΡ‰ΠΈΡ…ΡΡ

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    Π’Π΅Π΄ΡƒΡ‰ΡƒΡŽ Ρ€ΠΎΠ»ΡŒ Π² обСспСчСнии устойчивого ΡΠΎΡ†ΠΈΠ°Π»ΡŒΠ½ΠΎ-экономичСского развития страны ΠΈ Π°ΠΊΡ‚ΠΈΠ²Π½ΠΎΠ³ΠΎ наращивания чСловСчСского ΠΊΠ°ΠΏΠΈΡ‚Π°Π»Π° ΠΏΡƒΡ‚Π΅ΠΌ Π½Π΅ΠΏΡ€Π΅Ρ€Ρ‹Π²Π½ΠΎΠ³ΠΎ воспроизводства ΠΏΠ΅Ρ€Π΅Π΄ΠΎΠ²ΠΎΠ³ΠΎ знания Π² Ρ„ΠΎΡ€ΠΌΠ΅ исслСдований ΠΈ Ρ€Π°Π·Ρ€Π°Π±ΠΎΡ‚ΠΎΠΊ для ΠΏΡ€ΠΎΠΌΡ‹ΡˆΠ»Π΅Π½Π½ΠΎΡΡ‚ΠΈ ΠΈΠ³Ρ€Π°Π΅Ρ‚ ΠΏΠΎΠ΄Π³ΠΎΡ‚ΠΎΠ²ΠΊΠ° Π½ΠΎΠ²ΠΎΠ³ΠΎ поколСния аспирантов. ΠΠΊΡ‚ΡƒΠ°Π»ΡŒΠ½Ρ‹ΠΌΠΈ ΡΠ²Π»ΡΡŽΡ‚ΡΡ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Ρ‚Π΅Π»ΡŒΡΠΊΠΈΠ΅ вопросы: Π² ΡˆΠΈΡ€ΠΎΠΊΠΎΠΌ контСкстС β€” ΠΊΠ°ΠΊΠΎΠ²Π° Π΄ΠΈΠ½Π°ΠΌΠΈΠΊΠ° развития российской аспирантуры Π² Ρ€Π΅Π³ΠΈΠΎΠ½Π°Ρ… ΠΈ, Π² частности, β€” насколько Π±Π»Π°Π³ΠΎΠΏΠΎΠ»ΡƒΡ‡Π½Ρ‹ аспиранты, Ρ‡Ρ‚ΠΎΠ±Ρ‹ ΡƒΡΠΏΠ΅ΡˆΠ½ΠΎ Ρ€Π΅Π°Π»ΠΈΠ·ΠΎΠ²Ρ‹Π²Π°Ρ‚ΡŒ свои ΠΎΠ±Ρ€Π°Π·ΠΎΠ²Π°Ρ‚Π΅Π»ΡŒΠ½Ρ‹Π΅ Ρ‚Ρ€Π°Π΅ΠΊΡ‚ΠΎΡ€ΠΈΠΈ (трудоустроСны Π»ΠΈ, ΠΈΠΌΠ΅ΡŽΡ‚ Π»ΠΈ Π² этом ΠΏΠΎΡ‚Ρ€Π΅Π±Π½ΠΎΡΡ‚ΡŒ) ΠΈ ΠΊΠ°ΠΊΠΎΠ²Π° спСцифика ΠΎΠ±Ρ€Π°Π·ΠΎΠ²Π°Ρ‚Π΅Π»ΡŒΠ½ΠΎΠΉ ΠΌΠΎΠ±ΠΈΠ»ΡŒΠ½ΠΎΡΡ‚ΠΈ с Ρ†Π΅Π»ΡŒΡŽ получСния Π²Ρ‹ΡΡˆΠ΅ΠΉ ΠΊΠ²Π°Π»ΠΈΡ„ΠΈΠΊΠ°Ρ†ΠΈΠΈ. Научный интСрСс прСдставляСт ΠΈΠ·ΡƒΡ‡Π΅Π½ΠΈΠ΅ развития аспирантуры с ΡƒΡ‡Π΅Ρ‚ΠΎΠΌ распрСдСлСния рСсурсов (финансовых, ΠΈΠ½Ρ‚Π΅Π»Π»Π΅ΠΊΡ‚ΡƒΠ°Π»ΡŒΠ½Ρ‹Ρ…) ΠΏΠΎ Ρ€Π΅Π³ΠΈΠΎΠ½Π°ΠΌ, Π° Ρ‚Π°ΠΊΠΆΠ΅ ΠΈΡ… ΠΊΠΎΠ½Ρ†Π΅Π½Ρ‚Ρ€Π°Ρ†ΠΈΠΈ Π² Π²ΡƒΠ·Π°Ρ…, способных Π½Π° Π³Π΅Π½Π΅Ρ€Π°Ρ†ΠΈΡŽ ΠΏΡ€ΠΎΡ€Ρ‹Π²Π½Ρ‹Ρ… ΠΈΠ΄Π΅ΠΉ, Ρ‚Π΅Ρ…Π½ΠΎΠ»ΠΎΠ³ΠΈΠΉ Π² российской Π½Π°ΡƒΠΊΠ΅. Они Π²Ρ‹ΡΡ‚ΡƒΠΏΠ°ΡŽΡ‚ Ρ†Π΅Π½Ρ‚Ρ€Π°ΠΌΠΈ притяТСния ΠΏΡ€ΠΎΠ°ΠΊΡ‚ΠΈΠ²Π½ΠΎΠΉ ΠΌΠΎΠ»ΠΎΠ΄Π΅ΠΆΠΈ Π² Ρ€Π΅Π³ΠΈΠΎΠ½. Для ΠΎΠ±Ρ€Π°Π±ΠΎΡ‚ΠΊΠΈ ΠΈΠ½Ρ„ΠΎΡ€ΠΌΠ°Ρ†ΠΈΠΈ ΠΏΡ€ΠΈΠΌΠ΅Π½Π΅Π½Ρ‹ ΠΌΠ΅Ρ‚ΠΎΠ΄Ρ‹ Π²Ρ‚ΠΎΡ€ΠΈΡ‡Π½ΠΎΠ³ΠΎ Π°Π½Π°Π»ΠΈΠ·Π° Π΄Π°Π½Π½Ρ‹Ρ…, сопоставлСния, классификации, ΠΈΠ½Ρ„ΠΎΠ³Ρ€Π°Ρ„ΠΈΠΊΠΈ. ΠŸΡ€ΠΎΠ°Π½Π°Π»ΠΈΠ·ΠΈΡ€ΠΎΠ²Π°Π½Ρ‹ Π±Π°Π·Ρ‹ ΠΌΠΎΠ½ΠΈΡ‚ΠΎΡ€ΠΈΠ½Π³Π° эффСктивности Π²ΡƒΠ·ΠΎΠ² Π Π€ 2014–2020 Π³Π³., Ρ€Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚Ρ‹ социологичСских исслСдований аспирантов 2017–2020 Π³Π³. Π²Π΅Π΄ΡƒΡ‰Π΅Π³ΠΎ российского Π²ΡƒΠ·Π°. ΠŸΡ€Π΅Π΄Π»ΠΎΠΆΠ΅Π½Π° ΠΊΠ°Ρ€Ρ‚Π° ΠΊΠΎΠ½Ρ†Π΅Π½Ρ‚Ρ€Π°Ρ†ΠΈΠΈ аспирантов Π² ΠΎΡ‚Π΄Π΅Π»ΡŒΠ½Ρ‹Ρ… Ρ€Π΅Π³ΠΈΠΎΠ½Π°Ρ… с ΡƒΡ‡Π΅Ρ‚ΠΎΠΌ числСнности ΠΈ ΠΏΡ€ΠΈΡ‚ΠΎΠΊΠ° / ΠΎΡ‚Ρ‚ΠΎΠΊΠ°. Π¦Π΅Π½Ρ‚Ρ€Π°ΠΌΠΈ притяТСния аспирантов, ΠΏΠΎΠΌΠΈΠΌΠΎ столичных Ρ€Π΅Π³ΠΈΠΎΠ½ΠΎΠ² (ΠΈΡ… доля Π² ΠΎΠ±Ρ‰Π΅ΠΉ числСнности β€” 47,9 %), ΡΠ²Π»ΡΡŽΡ‚ΡΡ РСспублика Ватарстан (3,2 %), Вомская ΠΎΠ±Π»Π°ΡΡ‚ΡŒ (2,4 %), БвСрдловская ΠΎΠ±Π»Π°ΡΡ‚ΡŒ (2,1 %), Π³Π΄Π΅ располоТСны Π²Π΅Π΄ΡƒΡ‰ΠΈΠ΅ российскиС Π²ΡƒΠ·Ρ‹. Π’ Ρ‚ΠΎΠΏ 7 Ρ€Π΅Π³ΠΈΠΎΠ½ΠΎΠ² входят БСлгородская (доля аспирантов β€” 2,7 %) ΠΈ Ростовская области (2,4 %) с ΡΠΈΠ»ΡŒΠ½Ρ‹ΠΌΠΈ Π½Π°ΡƒΡ‡Π½ΠΎ-ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Ρ‚Π΅Π»ΡŒΡΠΊΠΈΠΌ ΠΈ Ρ„Π΅Π΄Π΅Ρ€Π°Π»ΡŒΠ½Ρ‹ΠΌ унивСрситСтами. Π”Π°ΠΆΠ΅ Π² Ρ€Π΅Π³ΠΈΠΎΠ½Π°Ρ…, ΠΏΡ€ΠΈΠ²Π»Π΅ΠΊΠ°ΡŽΡ‰ΠΈΡ… большоС количСство ΠΎΠ±ΡƒΡ‡Π°ΡŽΡ‰ΠΈΡ…ΡΡ, Π½Π°Π±Π»ΡŽΠ΄Π°Π΅Ρ‚ΡΡ ΠΈΡ… нСдофинансированиС Π² процСссС обучСния (85 % ΡΠΎΠ²ΠΌΠ΅Ρ‰Π°ΡŽΡ‚ Ρ€Π°Π±ΠΎΡ‚Ρƒ ΠΈ ΠΎΠ±ΡƒΡ‡Π΅Π½ΠΈΠ΅), растСт ΠΎΡ‚Ρ‚ΠΎΠΊ Π² Π·Π°Ρ€ΡƒΠ±Π΅ΠΆΠ½Ρ‹Π΅ Π²ΡƒΠ·Ρ‹. Π Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚Ρ‹ ΠΌΠΎΠ³ΡƒΡ‚ Π±Ρ‹Ρ‚ΡŒ ΠΈΡΠΏΠΎΠ»ΡŒΠ·ΠΎΠ²Π°Π½Ρ‹ для ΡΠΎΠ²Π΅Ρ€ΡˆΠ΅Π½ΡΡ‚Π²ΠΎΠ²Π°Π½ΠΈΡ ΠΏΡ€ΠΈΠ½Ρ†ΠΈΠΏΠΎΠ² развития аспирантуры ΠΈ ΠΌΠ΅Ρ…Π°Π½ΠΈΠ·ΠΌΠΎΠ² ΠΏΠΎΠ΄Π΄Π΅Ρ€ΠΆΠΊΠΈ аспирантов Π² Π²Π΅Π΄ΡƒΡ‰ΠΈΡ… ΠΈ ΠΈΠ½Ρ‹Ρ… Ρ€Π΅Π³ΠΈΠΎΠ½Π°Π»ΡŒΠ½Ρ‹Ρ… Π²ΡƒΠ·Π°Ρ… с Ρ†Π΅Π»ΡŒΡŽ устойчивого развития Ρ‚Π΅Ρ€Ρ€ΠΈΡ‚ΠΎΡ€ΠΈΠΉ

    УнивСрситСтский ΠΈ Ρ€Π΅Π³ΠΈΠΎΠ½Π°Π»ΡŒΠ½Ρ‹ΠΉ Π»Π°Π½Π΄ΡˆΠ°Ρ„Ρ‚ российской аспирантуры, финансовыС Ρ‚Ρ€Π°Π΅ΠΊΡ‚ΠΎΡ€ΠΈΠΈ ΠΎΠ±ΡƒΡ‡Π°ΡŽΡ‰ΠΈΡ…ΡΡ

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    Π’Π΅Π΄ΡƒΡ‰ΡƒΡŽ Ρ€ΠΎΠ»ΡŒ Π² обСспСчСнии устойчивого ΡΠΎΡ†ΠΈΠ°Π»ΡŒΠ½ΠΎ-экономичСского развития страны ΠΈ Π°ΠΊΡ‚ΠΈΠ²Π½ΠΎΠ³ΠΎ наращивания чСловСчСского ΠΊΠ°ΠΏΠΈΡ‚Π°Π»Π° ΠΏΡƒΡ‚Π΅ΠΌ Π½Π΅ΠΏΡ€Π΅Ρ€Ρ‹Π²Π½ΠΎΠ³ΠΎ воспроизводства ΠΏΠ΅Ρ€Π΅Π΄ΠΎΠ²ΠΎΠ³ΠΎ знания Π² Ρ„ΠΎΡ€ΠΌΠ΅ исслСдований ΠΈ Ρ€Π°Π·Ρ€Π°Π±ΠΎΡ‚ΠΎΠΊ для ΠΏΡ€ΠΎΠΌΡ‹ΡˆΠ»Π΅Π½Π½ΠΎΡΡ‚ΠΈ ΠΈΠ³Ρ€Π°Π΅Ρ‚ ΠΏΠΎΠ΄Π³ΠΎΡ‚ΠΎΠ²ΠΊΠ° Π½ΠΎΠ²ΠΎΠ³ΠΎ поколСния аспирантов. ΠΠΊΡ‚ΡƒΠ°Π»ΡŒΠ½Ρ‹ΠΌΠΈ ΡΠ²Π»ΡΡŽΡ‚ΡΡ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Ρ‚Π΅Π»ΡŒΡΠΊΠΈΠ΅ вопросы: Π² ΡˆΠΈΡ€ΠΎΠΊΠΎΠΌ контСкстС β€” ΠΊΠ°ΠΊΠΎΠ²Π° Π΄ΠΈΠ½Π°ΠΌΠΈΠΊΠ° развития российской аспирантуры Π² Ρ€Π΅Π³ΠΈΠΎΠ½Π°Ρ… ΠΈ, Π² частности, β€” насколько Π±Π»Π°Π³ΠΎΠΏΠΎΠ»ΡƒΡ‡Π½Ρ‹ аспиранты, Ρ‡Ρ‚ΠΎΠ±Ρ‹ ΡƒΡΠΏΠ΅ΡˆΠ½ΠΎ Ρ€Π΅Π°Π»ΠΈΠ·ΠΎΠ²Ρ‹Π²Π°Ρ‚ΡŒ свои ΠΎΠ±Ρ€Π°Π·ΠΎΠ²Π°Ρ‚Π΅Π»ΡŒΠ½Ρ‹Π΅ Ρ‚Ρ€Π°Π΅ΠΊΡ‚ΠΎΡ€ΠΈΠΈ (трудоустроСны Π»ΠΈ, ΠΈΠΌΠ΅ΡŽΡ‚ Π»ΠΈ Π² этом ΠΏΠΎΡ‚Ρ€Π΅Π±Π½ΠΎΡΡ‚ΡŒ) ΠΈ ΠΊΠ°ΠΊΠΎΠ²Π° спСцифика ΠΎΠ±Ρ€Π°Π·ΠΎΠ²Π°Ρ‚Π΅Π»ΡŒΠ½ΠΎΠΉ ΠΌΠΎΠ±ΠΈΠ»ΡŒΠ½ΠΎΡΡ‚ΠΈ с Ρ†Π΅Π»ΡŒΡŽ получСния Π²Ρ‹ΡΡˆΠ΅ΠΉ ΠΊΠ²Π°Π»ΠΈΡ„ΠΈΠΊΠ°Ρ†ΠΈΠΈ. Научный интСрСс прСдставляСт ΠΈΠ·ΡƒΡ‡Π΅Π½ΠΈΠ΅ развития аспирантуры с ΡƒΡ‡Π΅Ρ‚ΠΎΠΌ распрСдСлСния рСсурсов (финансовых, ΠΈΠ½Ρ‚Π΅Π»Π»Π΅ΠΊΡ‚ΡƒΠ°Π»ΡŒΠ½Ρ‹Ρ…) ΠΏΠΎ Ρ€Π΅Π³ΠΈΠΎΠ½Π°ΠΌ, Π° Ρ‚Π°ΠΊΠΆΠ΅ ΠΈΡ… ΠΊΠΎΠ½Ρ†Π΅Π½Ρ‚Ρ€Π°Ρ†ΠΈΠΈ Π² Π²ΡƒΠ·Π°Ρ…, способных Π½Π° Π³Π΅Π½Π΅Ρ€Π°Ρ†ΠΈΡŽ ΠΏΡ€ΠΎΡ€Ρ‹Π²Π½Ρ‹Ρ… ΠΈΠ΄Π΅ΠΉ, Ρ‚Π΅Ρ…Π½ΠΎΠ»ΠΎΠ³ΠΈΠΉ Π² российской Π½Π°ΡƒΠΊΠ΅. Они Π²Ρ‹ΡΡ‚ΡƒΠΏΠ°ΡŽΡ‚ Ρ†Π΅Π½Ρ‚Ρ€Π°ΠΌΠΈ притяТСния ΠΏΡ€ΠΎΠ°ΠΊΡ‚ΠΈΠ²Π½ΠΎΠΉ ΠΌΠΎΠ»ΠΎΠ΄Π΅ΠΆΠΈ Π² Ρ€Π΅Π³ΠΈΠΎΠ½. Для ΠΎΠ±Ρ€Π°Π±ΠΎΡ‚ΠΊΠΈ ΠΈΠ½Ρ„ΠΎΡ€ΠΌΠ°Ρ†ΠΈΠΈ ΠΏΡ€ΠΈΠΌΠ΅Π½Π΅Π½Ρ‹ ΠΌΠ΅Ρ‚ΠΎΠ΄Ρ‹ Π²Ρ‚ΠΎΡ€ΠΈΡ‡Π½ΠΎΠ³ΠΎ Π°Π½Π°Π»ΠΈΠ·Π° Π΄Π°Π½Π½Ρ‹Ρ…, сопоставлСния, классификации, ΠΈΠ½Ρ„ΠΎΠ³Ρ€Π°Ρ„ΠΈΠΊΠΈ. ΠŸΡ€ΠΎΠ°Π½Π°Π»ΠΈΠ·ΠΈΡ€ΠΎΠ²Π°Π½Ρ‹ Π±Π°Π·Ρ‹ ΠΌΠΎΠ½ΠΈΡ‚ΠΎΡ€ΠΈΠ½Π³Π° эффСктивности Π²ΡƒΠ·ΠΎΠ² Π Π€ 2014–2020 Π³Π³., Ρ€Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚Ρ‹ социологичСских исслСдований аспирантов 2017–2020 Π³Π³. Π²Π΅Π΄ΡƒΡ‰Π΅Π³ΠΎ российского Π²ΡƒΠ·Π°. ΠŸΡ€Π΅Π΄Π»ΠΎΠΆΠ΅Π½Π° ΠΊΠ°Ρ€Ρ‚Π° ΠΊΠΎΠ½Ρ†Π΅Π½Ρ‚Ρ€Π°Ρ†ΠΈΠΈ аспирантов Π² ΠΎΡ‚Π΄Π΅Π»ΡŒΠ½Ρ‹Ρ… Ρ€Π΅Π³ΠΈΠΎΠ½Π°Ρ… с ΡƒΡ‡Π΅Ρ‚ΠΎΠΌ числСнности ΠΈ ΠΏΡ€ΠΈΡ‚ΠΎΠΊΠ° / ΠΎΡ‚Ρ‚ΠΎΠΊΠ°. Π¦Π΅Π½Ρ‚Ρ€Π°ΠΌΠΈ притяТСния аспирантов, ΠΏΠΎΠΌΠΈΠΌΠΎ столичных Ρ€Π΅Π³ΠΈΠΎΠ½ΠΎΠ² (ΠΈΡ… доля Π² ΠΎΠ±Ρ‰Π΅ΠΉ числСнности β€” 47,9 %), ΡΠ²Π»ΡΡŽΡ‚ΡΡ РСспублика Ватарстан (3,2 %), Вомская ΠΎΠ±Π»Π°ΡΡ‚ΡŒ (2,4 %), БвСрдловская ΠΎΠ±Π»Π°ΡΡ‚ΡŒ (2,1 %), Π³Π΄Π΅ располоТСны Π²Π΅Π΄ΡƒΡ‰ΠΈΠ΅ российскиС Π²ΡƒΠ·Ρ‹. Π’ Ρ‚ΠΎΠΏ 7 Ρ€Π΅Π³ΠΈΠΎΠ½ΠΎΠ² входят БСлгородская (доля аспирантов β€” 2,7 %) ΠΈ Ростовская области (2,4 %) с ΡΠΈΠ»ΡŒΠ½Ρ‹ΠΌΠΈ Π½Π°ΡƒΡ‡Π½ΠΎ-ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Ρ‚Π΅Π»ΡŒΡΠΊΠΈΠΌ ΠΈ Ρ„Π΅Π΄Π΅Ρ€Π°Π»ΡŒΠ½Ρ‹ΠΌ унивСрситСтами. Π”Π°ΠΆΠ΅ Π² Ρ€Π΅Π³ΠΈΠΎΠ½Π°Ρ…, ΠΏΡ€ΠΈΠ²Π»Π΅ΠΊΠ°ΡŽΡ‰ΠΈΡ… большоС количСство ΠΎΠ±ΡƒΡ‡Π°ΡŽΡ‰ΠΈΡ…ΡΡ, Π½Π°Π±Π»ΡŽΠ΄Π°Π΅Ρ‚ΡΡ ΠΈΡ… нСдофинансированиС Π² процСссС обучСния (85 % ΡΠΎΠ²ΠΌΠ΅Ρ‰Π°ΡŽΡ‚ Ρ€Π°Π±ΠΎΡ‚Ρƒ ΠΈ ΠΎΠ±ΡƒΡ‡Π΅Π½ΠΈΠ΅), растСт ΠΎΡ‚Ρ‚ΠΎΠΊ Π² Π·Π°Ρ€ΡƒΠ±Π΅ΠΆΠ½Ρ‹Π΅ Π²ΡƒΠ·Ρ‹. Π Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚Ρ‹ ΠΌΠΎΠ³ΡƒΡ‚ Π±Ρ‹Ρ‚ΡŒ ΠΈΡΠΏΠΎΠ»ΡŒΠ·ΠΎΠ²Π°Π½Ρ‹ для ΡΠΎΠ²Π΅Ρ€ΡˆΠ΅Π½ΡΡ‚Π²ΠΎΠ²Π°Π½ΠΈΡ ΠΏΡ€ΠΈΠ½Ρ†ΠΈΠΏΠΎΠ² развития аспирантуры ΠΈ ΠΌΠ΅Ρ…Π°Π½ΠΈΠ·ΠΌΠΎΠ² ΠΏΠΎΠ΄Π΄Π΅Ρ€ΠΆΠΊΠΈ аспирантов Π² Π²Π΅Π΄ΡƒΡ‰ΠΈΡ… ΠΈ ΠΈΠ½Ρ‹Ρ… Ρ€Π΅Π³ΠΈΠΎΠ½Π°Π»ΡŒΠ½Ρ‹Ρ… Π²ΡƒΠ·Π°Ρ… с Ρ†Π΅Π»ΡŒΡŽ устойчивого развития Ρ‚Π΅Ρ€Ρ€ΠΈΡ‚ΠΎΡ€ΠΈΠΉ

    Differentiation of universities by the level of teaching staff income: Correlation with the quality of education and research productivity

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    A significant indicator for assessing the Russian universities' performance is the ratio of teaching staff income to the regional average. The study allows obtaining an objective picture of the diversity of Russian higher education institutions that determines the principles of their further cooperation and concentration of human resources (teachers, students) in those universities where salaries are above the regional average. The research problem is to identify the factors of differentiation of universities' differentiation that have a decisive effect on their type, taking into account the level of teaching staff income and the orientation of universities (focus on educational or research activity). The authors analyze the cases of 769 universities included in the 2017 database for monitoring the Russian Federation universities' performance. To process the obtained data, the methods of factor analysis, classification, and single-factor analysis of variance were used. A comparative analysis of university cases showed that 74% of universities teaching staff members are financially successful, and in 26% - unsuccessful. The universities that focus on research (factor significance = 0.696) and scientific (factor significance = 0.642) developments are both β€œsmart” and β€œrich”. The institutions that focus on education can be β€œrich” in modern realities if they have a significant capacity in terms of publishing activity (factor significance = 0.322) without imposing strict requirements on participation in research for all teaching staff members. The results of the study have practical implications for improving the educational and research ecosystem benchmarks at the level of individual regions based on balancing university management tools taking into account their strengths (education, research), which will allow choosing strategies to enhance the economic growth of specific universities and to increase the human potential of the region as a whole. Β© 2019 LLC Ecological Help. All rights reserved

    Immune pleiotropic effect of telmisartan in arterial hypertension

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    Arterial hypertension (AH) is among the life-threatening diseases and requires permanent antihypertensive therapy, including telmisartan. However, the effect of telmisartan upon systemic interleukin profile in elderly hypertensive patients requires further study, due to the limited data on previously analyzed interleukins. The aim of our study was to evaluate the immune pleiotropic effect of telmisartan upon miultiple pro- and anti-inflammatory blood interleukins in the patients with hypertension. The study included examination of 74 patients aged 60-74 years suffering from hypertension treated with telmisartan (80 mg/day in the morning time). The immune response to telmisartan assessed by the blood contents of different interleukins was evaluated following 6 months of treatment. These markers were determined by flow cytometry using β€œBecton Dickinson FACS Canto 2” device (USA). The pleiotropic immune effect of telmisartan upon the interleukin profile in hypertensive patients aged 60-74 was established by statistically significant changes in multiple pro-inflammatory and anti-inflammatory interleukins. Following 6 months of telmisartan therapy, the patients with arterial hypertension have shown a statistically significant decrease in blood cytokines, i.e., IL-1 Π² was reduced to 8.1Β±0.6 pg/ml vs initial 10.5Β±0.8 pg/ml; IL-2, to 8.6Β±0.8 pg/ml vs initial 11.8Β±1.1 pg/ml; IL-6, to 18.4Β±0.5 pg/ml vs initial 21.2Β±0.7 pg/ml; IL-8, to 3.5Β±0.6 pg/ml vs 5.4Β±0.5 pg/ml. We have also revealed a statistically significant decrease of blood TNFΞ± levels to 5.3Β±0.5 pg/ml versus initial 6.8Β±0.4 pg/ml in the elderly patients with hypertension after 6 months of antihypertensive therapy with telmisartan. Moreover, the levels of pro-inflammatory systemic interleukins and, especially, IL-4 showed an increase from 4.6Β±0.5 pg/ml to 7.0Β±0.6 pg/ml in the course of telmisartan therapy in these patients. In summary, one may suggest that telmisartan exerts a significant immune pleiotropic effect in the patients with hypertension, confirmed by the systemic changes of interleukin contents. The pleiotropic effects of telmisartan have been established in patients with arterial hypertension, expressed as a significant decrease in IL-1, IL-2, IL-6, IL-8, TNFΞ± levels, along with increased IL-4 and IL-10 contents. The results obtained showed a significant pleiotropic effect of telmisartan in the patients with arterial hypertension upon several interleukins, thus expanding the role of immune inflammation in this disorder, as well as its reversal with telmisartan therapy

    Infiornativity lacrimal fluid interleukins in diagnostics and development of angle-closure glaucoma in elderly subjects

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    An increased percentage of the elderly subjects in pattern of contemporary society, along with other causes and risk factors, is accompanied by rise in the incidence of glaucoma. By 2020, according to international studies it is expected that prevalence of glaucoma patients in the world would increase up to 80 million subjects. Among the elderly, glaucoma is a common pathology, which development is associated with local disturbances in interleukin profile. However, the features of the latter in patients with primary closed-angle glaucoma in the elderly were poorly examined. Studies of local interleukin status were conducted mainly in patients with suspected or initial manifestations of primary open-angle glaucoma. The features of lacrimal fluid interleukin shift in a target group of elderly patients suffering from stage II primary closed-angle glaucoma virtually gained no attention. In addition, a limited range of local interleukins in patients with such pathology in previous studies was examined. In addition, informativity of lacrimal fluid interleukins in elderly glaucoma patients was not assessed too based on objective methods. The aim of the current study was to outline features and informativity of local interleukin profile indicators in 58 elderly patients with primary closed-angle glaucoma stage II, aged 60β€”74 years (main group) and 27 age-matched elderly subjects lacking such pathology. The level of interleukins in the lacrimal fluid was determined with the enzyme immunoassay β€œMultiscan” analyzer (Finland) by using sandwich ELISA (R&D Diagnostic Inc., USA). Informativity of measuring various interleukins was calculated according to the generally accepted formula. It was found that local interleukin profile in elderly patients with primary closed-angle glaucoma was mainly featured with increased amount of IL-2, IL-17, IL-8, but decreased IL-10. Hence, such local interleukins displayed peak informativity. The data obtained should be used in the diagnostics and treatment of such pathology, as well as of applied importance to unveil novel mechanisms behind development, diagnostics and corroboration for selective immuno-tropic therapy of primary closed-angle glaucoma

    University and Regional Landscape of Doctoral Studies in Russia: Financial Trajectories of Graduate Students

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    Π’Π΅Π΄ΡƒΡ‰ΡƒΡŽ Ρ€ΠΎΠ»ΡŒ Π² обСспСчСнии устойчивого ΡΠΎΡ†ΠΈΠ°Π»ΡŒΠ½ΠΎ-экономичСского развития страны ΠΈ Π°ΠΊΡ‚ΠΈΠ²Π½ΠΎΠ³ΠΎ наращивания чСловСчСского ΠΊΠ°ΠΏΠΈΡ‚Π°Π»Π° ΠΏΡƒΡ‚Π΅ΠΌ Π½Π΅ΠΏΡ€Π΅Ρ€Ρ‹Π²Π½ΠΎΠ³ΠΎ воспроизводства ΠΏΠ΅Ρ€Π΅Π΄ΠΎΠ²ΠΎΠ³ΠΎ знания Π² Ρ„ΠΎΡ€ΠΌΠ΅ исслСдований ΠΈ Ρ€Π°Π·Ρ€Π°Π±ΠΎΡ‚ΠΎΠΊ для ΠΏΡ€ΠΎΠΌΡ‹ΡˆΠ»Π΅Π½Π½ΠΎΡΡ‚ΠΈ ΠΈΠ³Ρ€Π°Π΅Ρ‚ ΠΏΠΎΠ΄Π³ΠΎΡ‚ΠΎΠ²ΠΊΠ° Π½ΠΎΠ²ΠΎΠ³ΠΎ поколСния аспирантов. ΠΠΊΡ‚ΡƒΠ°Π»ΡŒΠ½Ρ‹ΠΌΠΈ ΡΠ²Π»ΡΡŽΡ‚ΡΡ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Ρ‚Π΅Π»ΡŒΡΠΊΠΈΠ΅ вопросы: Π² ΡˆΠΈΡ€ΠΎΠΊΠΎΠΌ контСкстС - ΠΊΠ°ΠΊΠΎΠ²Π° Π΄ΠΈΠ½Π°ΠΌΠΈΠΊΠ° развития российской аспирантуры Π² Ρ€Π΅Π³ΠΈΠΎΠ½Π°Ρ… ΠΈ, Π² частности, - насколько Π±Π»Π°Π³ΠΎΠΏΠΎΠ»ΡƒΡ‡Π½Ρ‹ аспиранты, Ρ‡Ρ‚ΠΎΠ±Ρ‹ ΡƒΡΠΏΠ΅ΡˆΠ½ΠΎ Ρ€Π΅Π°Π»ΠΈΠ·ΠΎΠ²Ρ‹Π²Π°Ρ‚ΡŒ свои ΠΎΠ±Ρ€Π°Π·ΠΎΠ²Π°Ρ‚Π΅Π»ΡŒΠ½Ρ‹Π΅ Ρ‚Ρ€Π°Π΅ΠΊΡ‚ΠΎΡ€ΠΈΠΈ (трудоустроСны Π»ΠΈ, ΠΈΠΌΠ΅ΡŽΡ‚ Π»ΠΈ Π² этом ΠΏΠΎΡ‚Ρ€Π΅Π±Π½ΠΎΡΡ‚ΡŒ) ΠΈ ΠΊΠ°ΠΊΠΎΠ²Π° спСцифика ΠΎΠ±Ρ€Π°Π·ΠΎΠ²Π°Ρ‚Π΅Π»ΡŒΠ½ΠΎΠΉ ΠΌΠΎΠ±ΠΈΠ»ΡŒΠ½ΠΎΡΡ‚ΠΈ с Ρ†Π΅Π»ΡŒΡŽ получСния Π²Ρ‹ΡΡˆΠ΅ΠΉ ΠΊΠ²Π°Π»ΠΈΡ„ΠΈΠΊΠ°Ρ†ΠΈΠΈ. Научный интСрСс прСдставляСт ΠΈΠ·ΡƒΡ‡Π΅Π½ΠΈΠ΅ развития аспирантуры с ΡƒΡ‡Π΅Ρ‚ΠΎΠΌ распрСдСлСния рСсурсов (финансовых, ΠΈΠ½Ρ‚Π΅Π»Π»Π΅ΠΊΡ‚ΡƒΠ°Π»ΡŒΠ½Ρ‹Ρ…) ΠΏΠΎ Ρ€Π΅Π³ΠΈΠΎΠ½Π°ΠΌ, Π° Ρ‚Π°ΠΊΠΆΠ΅ ΠΈΡ… ΠΊΠΎΠ½Ρ†Π΅Π½Ρ‚Ρ€Π°Ρ†ΠΈΠΈ Π² Π²ΡƒΠ·Π°Ρ…, способных Π½Π° Π³Π΅Π½Π΅Ρ€Π°Ρ†ΠΈΡŽ ΠΏΡ€ΠΎΡ€Ρ‹Π²Π½Ρ‹Ρ… ΠΈΠ΄Π΅ΠΉ, Ρ‚Π΅Ρ…Π½ΠΎΠ»ΠΎΠ³ΠΈΠΉ Π² российской Π½Π°ΡƒΠΊΠ΅. Они Π²Ρ‹ΡΡ‚ΡƒΠΏΠ°ΡŽΡ‚ Ρ†Π΅Π½Ρ‚Ρ€Π°ΠΌΠΈ притяТСния ΠΏΡ€ΠΎΠ°ΠΊΡ‚ΠΈΠ²Π½ΠΎΠΉ ΠΌΠΎΠ»ΠΎΠ΄Π΅ΠΆΠΈ Π² Ρ€Π΅Π³ΠΈΠΎΠ½. Для ΠΎΠ±Ρ€Π°Π±ΠΎΡ‚ΠΊΠΈ ΠΈΠ½Ρ„ΠΎΡ€ΠΌΠ°Ρ†ΠΈΠΈ ΠΏΡ€ΠΈΠΌΠ΅Π½Π΅Π½Ρ‹ ΠΌΠ΅Ρ‚ΠΎΠ΄Ρ‹ Π²Ρ‚ΠΎΡ€ΠΈΡ‡Π½ΠΎΠ³ΠΎ Π°Π½Π°Π»ΠΈΠ·Π° Π΄Π°Π½Π½Ρ‹Ρ…, сопоставлСния, классификации, ΠΈΠ½Ρ„ΠΎΠ³Ρ€Π°Ρ„ΠΈΠΊΠΈ. ΠŸΡ€ΠΎΠ°Π½Π°Π»ΠΈΠ·ΠΈΡ€ΠΎΠ²Π°Π½Ρ‹ Π±Π°Π·Ρ‹ ΠΌΠΎΠ½ΠΈΡ‚ΠΎΡ€ΠΈΠ½Π³Π° эффСктивности Π²ΡƒΠ·ΠΎΠ² Π Π€ 2014-2020 Π³Π³., Ρ€Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚Ρ‹ социологичСских исслСдований аспирантов 2017-2020 Π³Π³. Π²Π΅Π΄ΡƒΡ‰Π΅Π³ΠΎ российского Π²ΡƒΠ·Π°. ΠŸΡ€Π΅Π΄Π»ΠΎΠΆΠ΅Π½Π° ΠΊΠ°Ρ€Ρ‚Π° ΠΊΠΎΠ½Ρ†Π΅Π½Ρ‚Ρ€Π°Ρ†ΠΈΠΈ аспирантов Π² ΠΎΡ‚Π΄Π΅Π»ΡŒΠ½Ρ‹Ρ… Ρ€Π΅Π³ΠΈΠΎΠ½Π°Ρ… с ΡƒΡ‡Π΅Ρ‚ΠΎΠΌ числСнности ΠΈ ΠΏΡ€ΠΈΡ‚ΠΎΠΊΠ° / ΠΎΡ‚Ρ‚ΠΎΠΊΠ°. Π¦Π΅Π½Ρ‚Ρ€Π°ΠΌΠΈ притяТСния аспирантов, ΠΏΠΎΠΌΠΈΠΌΠΎ столичных Ρ€Π΅Π³ΠΈΠΎΠ½ΠΎΠ² (ΠΈΡ… доля Π² ΠΎΠ±Ρ‰Π΅ΠΉ числСнности - 47,9 %), ΡΠ²Π»ΡΡŽΡ‚ΡΡ РСспублика Ватарстан (3,2 %), Вомская ΠΎΠ±Π»Π°ΡΡ‚ΡŒ (2,4 %), БвСрдловская ΠΎΠ±Π»Π°ΡΡ‚ΡŒ (2,1 %), Π³Π΄Π΅ располоТСны Π²Π΅Π΄ΡƒΡ‰ΠΈΠ΅ российскиС Π²ΡƒΠ·Ρ‹. Π’ Ρ‚ΠΎΠΏ 7 Ρ€Π΅Π³ΠΈΠΎΠ½ΠΎΠ² входят БСлгородская (доля аспирантов - 2,7 %) ΠΈ Ростовская области (2,4 %) с ΡΠΈΠ»ΡŒΠ½Ρ‹ΠΌΠΈ Π½Π°ΡƒΡ‡Π½ΠΎ-ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Ρ‚Π΅Π»ΡŒΡΠΊΠΈΠΌ ΠΈ Ρ„Π΅Π΄Π΅Ρ€Π°Π»ΡŒΠ½Ρ‹ΠΌ унивСрситСтами. Π”Π°ΠΆΠ΅ Π² Ρ€Π΅Π³ΠΈΠΎΠ½Π°Ρ…, ΠΏΡ€ΠΈΠ²Π»Π΅ΠΊΠ°ΡŽΡ‰ΠΈΡ… большоС количСство ΠΎΠ±ΡƒΡ‡Π°ΡŽΡ‰ΠΈΡ…ΡΡ, Π½Π°Π±Π»ΡŽΠ΄Π°Π΅Ρ‚ΡΡ ΠΈΡ… нСдофинансированиС Π² процСссС обучСния (85 % ΡΠΎΠ²ΠΌΠ΅Ρ‰Π°ΡŽΡ‚ Ρ€Π°Π±ΠΎΡ‚Ρƒ ΠΈ ΠΎΠ±ΡƒΡ‡Π΅Π½ΠΈΠ΅), растСт ΠΎΡ‚Ρ‚ΠΎΠΊ Π² Π·Π°Ρ€ΡƒΠ±Π΅ΠΆΠ½Ρ‹Π΅ Π²ΡƒΠ·Ρ‹. Π Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚Ρ‹ ΠΌΠΎΠ³ΡƒΡ‚ Π±Ρ‹Ρ‚ΡŒ ΠΈΡΠΏΠΎΠ»ΡŒΠ·ΠΎΠ²Π°Π½Ρ‹ для ΡΠΎΠ²Π΅Ρ€ΡˆΠ΅Π½ΡΡ‚Π²ΠΎΠ²Π°Π½ΠΈΡ ΠΏΡ€ΠΈΠ½Ρ†ΠΈΠΏΠΎΠ² развития аспирантуры ΠΈ ΠΌΠ΅Ρ…Π°Π½ΠΈΠ·ΠΌΠΎΠ² ΠΏΠΎΠ΄Π΄Π΅Ρ€ΠΆΠΊΠΈ аспирантов Π² Π²Π΅Π΄ΡƒΡ‰ΠΈΡ… ΠΈ ΠΈΠ½Ρ‹Ρ… Ρ€Π΅Π³ΠΈΠΎΠ½Π°Π»ΡŒΠ½Ρ‹Ρ… Π²ΡƒΠ·Π°Ρ… с Ρ†Π΅Π»ΡŒΡŽ устойчивого развития Ρ‚Π΅Ρ€Ρ€ΠΈΡ‚ΠΎΡ€ΠΈΠΉ.Training of a new generation of graduate students plays a key role in ensuring a country’s sustainable socio-economic development and active enhancement of human capital by continuous reproduction of cutting-edge knowledge in the form of research and development (R&D) for industry. In this context, it becomes important to examine the development dynamics of doctoral studies in Russian regions, as well as the graduate students’ financial well-being affecting their educational mobility and general opportunities to receive education (in particular, necessity of employment). The development of doctoral studies is analysed taking into account the distribution of resources (financial, intellectual) by regions and universities. The study also considers the concentration of resources in Russian universities capable of generating breakthrough ideas and technologies, which can be seen as centres of attraction for proactive youth. The methods of secondary data analysis, comparison, classification, and infographics were applied to process information. Such data as the monitoring of the effectiveness of Russian universities in 2014-2020 and sociological surveys of graduate students of a leading Russian university for 2017-2020 were analysed. As a result, the study presents a map showing the concentration of graduate students in certain regions, which takes into consideration their number, inflow and outflow. In addition to the capital regions (their share is 47.9 %), the Republic of Tatarstan (3.2 %), Tomsk oblast (2.4 %) and Sverdlovsk oblast (2.1 %), where leading Russian universities are located, were revealed to be the centres for attraction of graduate students. The top 7 regions also include Belgorod (the share of graduate students is 2.7 %) and Rostov oblasts (2.4 %) characterised by the presence of strong research and federal universities. However, due to the lack of funding (85 % of graduate students have to combine work and studies), the outflow to foreign universities is increasing even in the regions that attract a large number of scholars. The obtained findings can be used to improve the mechanisms for supporting graduate students in order to contribute to sustainable development of regions.ИсслСдованиС Π²Ρ‹ΠΏΠΎΠ»Π½Π΅Π½ΠΎ ΠΏΡ€ΠΈ финансовой ΠΏΠΎΠ΄Π΄Π΅Ρ€ΠΆΠΊΠ΅ ΠœΠΈΠ½ΠΈΡΡ‚Π΅Ρ€ΡΡ‚Π²Π° Π½Π°ΡƒΠΊΠΈ ΠΈ Π²Ρ‹ΡΡˆΠ΅Π³ΠΎ образования Российской Π€Π΅Π΄Π΅Ρ€Π°Ρ†ΠΈΠΈ Π² Ρ€Π°ΠΌΠΊΠ°Ρ… ΠŸΡ€ΠΎΠ³Ρ€Π°ΠΌΠΌΡ‹ развития Π£Ρ€Π°Π»ΡŒΡΠΊΠΎΠ³ΠΎ Ρ„Π΅Π΄Π΅Ρ€Π°Π»ΡŒΠ½ΠΎΠ³ΠΎ унивСрситСта ΠΈΠΌΠ΅Π½ΠΈ ΠΏΠ΅Ρ€Π²ΠΎΠ³ΠΎ ΠŸΡ€Π΅Π·ΠΈΠ΄Π΅Π½Ρ‚Π° России Π‘. Н. Π•Π»ΡŒΡ†ΠΈΠ½Π° Π² соотвСтствии с ΠΏΡ€ΠΎΠ³Ρ€Π°ΠΌΠΌΠΎΠΉ стратСгичСского акадСмичСского лидСрства Β«ΠŸΡ€ΠΈΠΎΡ€ΠΈΡ‚Π΅Ρ‚-2030Β».The article has been prepared with the support of the Ministry of Science and Higher Education of the Russian Federation within the framework of the development program of the Ural Federal University as part of the strategic academic leadership program Β«Priority 2030Β»

    Precarisation of labour as a growing form of employment of young specialists in the context of the Π‘ovid-19 pandemic

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    ПандСмия стала ΠΊΠ°Ρ‚Π°Π»ΠΈΠ·Π°Ρ‚ΠΎΡ€ΠΎΠΌ Π½Π΅ΠΈΠ·Π±Π΅ΠΆΠ½ΠΎΠ³ΠΎ процСсса Ρ†ΠΈΡ„Ρ€ΠΎΠ²ΠΈΠ·Π°Ρ†ΠΈΠΈ ΠΊΠΎΠΌΠΌΡƒΠ½ΠΈΠΊΠ°Ρ†ΠΈΠΉ, ΡΡ‚Ρ€Π΅ΠΌΠΈΡ‚Π΅Π»ΡŒΠ½ΠΎ ΠΈΠ·ΠΌΠ΅Π½ΠΈΠ² ΠΎΡ€Π³Π°Π½ΠΈΠ·Π°Ρ†ΠΈΡŽ ΠΈ Ρ‚Π΅Ρ…Π½ΠΎΠ»ΠΎΠ³ΠΈΠΈ Ρ€Π΅Π°Π»ΠΈΠ·Π°Ρ†ΠΈΠΈ ΠΏΡ€ΠΎΡ„Π΅ΡΡΠΈΠΎΠ½Π°Π»ΡŒΠ½ΠΎΠΉ Π΄Π΅ΡΡ‚Π΅Π»ΡŒΠ½ΠΎΡΡ‚ΠΈ ΠΌΠΈΠ»Π»ΠΈΠΎΠ½ΠΎΠ² сотрудников ΠΏΠΎ всСму ΠΌΠΈΡ€Ρƒ. Рост Π±Π΅Π·Ρ€Π°Π±ΠΎΡ‚ΠΈΡ†Ρ‹, ΠΏΠ΅Ρ€Π΅Π²ΠΎΠ΄ всСх Π²ΠΎΠ·ΠΌΠΎΠΆΠ½Ρ‹Ρ… ΠΏΡ€ΠΎΡ„Π΅ΡΡΠΈΠΎΠ½Π°Π»ΡŒΠ½Ρ‹Ρ… Π³Ρ€ΡƒΠΏΠΏ Π½Π° Π΄ΠΈΡΡ‚Π°Π½Ρ†ΠΈΠΎΠ½Π½ΡƒΡŽ Ρ€Π°Π±ΠΎΡ‚Ρƒ, обусловлСнныС внСшнСй Π½Π΅ΠΎΠ±Ρ…ΠΎΠ΄ΠΈΠΌΠΎΡΡ‚ΡŒΡŽ Π² изоляции для ΠΌΠΈΠ½ΠΈΠΌΠΈΠ·Π°Ρ†ΠΈΠΈ распространСния Π² 2020 Π³ΠΎΠ΄Ρƒ Covid-19, Π²Π΅Π΄ΡƒΡ‚ ΠΊ Ρ€Π°Π΄ΠΈΠΊΠ°Π»ΡŒΠ½ΠΎΠΌΡƒ измСнСнию Ρ€Ρ‹Π½ΠΊΠ° Ρ‚Ρ€ΡƒΠ΄Π°. Поиск ΠΎΡ‚Π²Π΅Ρ‚ΠΎΠ² Π½Π° Π½ΠΎΠ²Ρ‹Π΅ Π²Ρ‹Π·ΠΎΠ²Ρ‹ дСрСгуляции Ρ‚Ρ€ΡƒΠ΄ΠΎΠ²Ρ‹Ρ… ΠΎΡ‚Π½ΠΎΡˆΠ΅Π½ΠΈΠΉ Π²ΠΎΠ·ΠΌΠΎΠΆΠ΅Π½ ΠΏΡƒΡ‚Π΅ΠΌ изучСния процСссов ΠΏΡ€Π΅ΠΊΠ°Ρ€ΠΈΠ·Π°Ρ†ΠΈΠΈ Π½Π° Ρ€Ρ‹Π½ΠΊΠ΅ Ρ‚Ρ€ΡƒΠ΄Π°. Научный интСрСс прСдставляСт ΠΈΠ·ΡƒΡ‡Π΅Π½ΠΈΠ΅ участия Π² этих процСссах ΠΌΠΎΠ»ΠΎΠ΄Π΅ΠΆΠΈ, которая восприимчива ΠΊ ΡΠΎΡ†ΠΈΠ°Π»ΡŒΠ½Ρ‹ΠΌ инновациям ΠΈ ΠΎΠ±Π»Π°Π΄Π°Π΅Ρ‚ высокими компСтСнциями Π² ΠΈΠ½Ρ„ΠΎΡ€ΠΌΠ°Ρ†ΠΈΠΎΠ½Π½Ρ‹Ρ… тСхнологиях. ИсслСдованиС ΠΏΡ€ΠΎΡ„Π΅ΡΡΠΈΠΎΠ½Π°Π»ΡŒΠ½Ρ‹Ρ… Ρ‚Ρ€Π°Π΅ΠΊΡ‚ΠΎΡ€ΠΈΠΉ выпускников Π²ΡƒΠ·ΠΎΠ², трудоустроСнных Π² Π£Ρ€Π°Π»ΡŒΡΠΊΠΎΠΌ Ρ€Π΅Π³ΠΈΠΎΠ½Π΅ ΠΈ Π·Π° Π΅Π³ΠΎ ΠΏΡ€Π΅Π΄Π΅Π»Π°ΠΌΠΈ, позволяСт Π²Ρ‹ΡΠ²ΠΈΡ‚ΡŒ, ΠΌΠΎΠ³ΡƒΡ‚ Π»ΠΈ Π±Ρ‹Ρ‚ΡŒ ΡƒΡΠΏΠ΅ΡˆΠ½Ρ‹ Ρ„ΠΎΡ€ΠΌΡ‹ ΠΏΡ€Π΅ΠΊΠ°Ρ€Π½ΠΎΠΉ занятости Π½Π° Ρ€Ρ‹Π½ΠΊΠ΅ Ρ‚Ρ€ΡƒΠ΄Π°, ΠΊΠ°ΠΊΠΎΠ²Ρ‹ характСристики Π²ΠΎΠ²Π»Π΅Ρ‡Π΅Π½Π½Ρ‹Ρ… Π² эти Ρ„ΠΎΡ€ΠΌΡ‹ ΡΠΎΡ†ΠΈΠ°Π»ΡŒΠ½Ρ‹Ρ… Π³Ρ€ΡƒΠΏΠΏ. Авторы ΠΈΡΠΏΠΎΠ»ΡŒΠ·ΡƒΡŽΡ‚ ΠΌΠΎΠ½ΠΈΡ‚ΠΎΡ€ΠΈΠ½Π³ выпускников Π²ΡƒΠ·Π° 2017-2019 Π³Π³., ΠΏΡ€ΠΎΠ²Π΅Π΄Π΅Π½Π½Ρ‹ΠΉ Π½Π° основС опросных ΠΈ административных Π΄Π°Π½Π½Ρ‹Ρ…. Для ΠΎΠ±Ρ€Π°Π±ΠΎΡ‚ΠΊΠΈ ΠΈΠ½Ρ„ΠΎΡ€ΠΌΠ°Ρ†ΠΈΠΈ ΠΈΡΠΏΠΎΠ»ΡŒΠ·ΠΎΠ²Π°Π½Ρ‹ ΠΌΠ΅Ρ‚ΠΎΠ΄Ρ‹ классификации, экспСртных ΠΎΡ†Π΅Π½ΠΎΠΊ. Анализ ΠΏΠΎΠΊΠ°Π·Π°Π», Ρ‡Ρ‚ΠΎ 34,4 % выпускников Π²ΡƒΠ·Π° относятся ΠΊ ΠΏΡ€Π΅ΠΊΠ°Ρ€ΠΈΠ°Ρ‚Ρƒ, ΠΈΠ· Π½ΠΈΡ… лишь 8,8 % - Π±Π΅Π·Ρ€Π°Π±ΠΎΡ‚Π½Ρ‹Π΅. ΠœΠΎΠ»ΠΎΠ΄Ρ‹Π΅ спСциалисты, занятыС Π² Ρ„ΠΎΡ€ΠΌΠ΅ фриланса ΠΈ Π² сфСрС IT-Ρ‚Π΅Ρ…Π½ΠΎΠ»ΠΎΠ³ΠΈΠΉ, ΡƒΡΠΏΠ΅ΡˆΠ½ΠΎ трудоустроСны, ΠΈΠΌΠ΅ΡŽΡ‚ высокиС ΠΏΠΎΠΊΠ°Π·Π°Ρ‚Π΅Π»ΠΈ трудоустройства ΠΏΠΎ ΡΠΏΠ΅Ρ†ΠΈΠ°Π»ΡŒΠ½ΠΎΡΡ‚ΠΈ, Π·Π°Ρ€Π°Π±ΠΎΡ‚Π½ΠΎΠΉ ΠΏΠ»Π°Ρ‚Π΅, удовлСтворСнности Ρ€Π°Π±ΠΎΡ‚ΠΎΠΉ. Π Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚Ρ‹ ΠΏΡ€ΠΈΠΌΠ΅Π½ΠΈΠΌΡ‹ для балансировки нСустойчивой занятости ΠΈ использования Π΅Π΅ Π»ΡƒΡ‡ΡˆΠΈΡ… ΠΏΡ€Π°ΠΊΡ‚ΠΈΠΊ, Π½Π°ΠΊΠΎΠΏΠ»Π΅Π½Π½Ρ‹Ρ… фрилансСрами ΠΈ спСциалистами Π² сфСрС IT-Ρ‚Π΅Ρ…Π½ΠΎΠ»ΠΎΠ³ΠΈΠΉ, ΠΊΠ°ΠΊ ΡΠΎΡ†ΠΈΠ°Π»ΡŒΠ½ΠΎΠ³ΠΎ инструмСнта рСгулирования Ρ‚Ρ€ΡƒΠ΄ΠΎΠ²Ρ‹Ρ… ΠΎΡ‚Π½ΠΎΡˆΠ΅Π½ΠΈΠΉ Π² слоТной эпидСмиологичСской ситуации.The Π‘ovid-19 pandemic has catalysed the inevitable digitalisation of communications and rapidly changed the organisation and technologies of professional activities of millions of employees worldwide. The growth of unemployment, the transition of professional groups to remote work (wherever possible) due to the need for isolation to minimise the spread of COVID-19 in 2020 led to radical changes in the labour market. Studying the processes of precariation can facilitate the search for responses to new challenges related to deregulation of labour relations. We are interested in examining the participation of youth in these processes. Young population is receptive to social innovation and has excellent competencies in the field of information technology. An analysis of professional trajectories of university graduates (employed in the Ural region and beyond) helps identify whether precarious employment in the labour market can be successful, and determine the characteristics of social groups involved. We used the monitoring of university graduates conducted in 2017-2019 based on survey and administrative data. To process the data, we applied the methods of classification and expert evaluations. The analysis showed that 34.4 % of university graduates belong to the precariat, with only 8.8 % being unemployed. Young freelancers and IT-professionals are successfully employed, satisfied with their jobs and high salaries, demonstrating high rates of employment in their specialty. The results can be applied for balancing precarious work; its best practices, accumulated by freelancers and IT-professionals, can be used as a social tool for regulating labour relations in an unfavourable epidemiological situation.ИсслСдованиС ΠΏΠΎΠ΄Π΄Π΅Ρ€ΠΆΠ°Π½ΠΎ ΠΏΡ€ΠΎΠ³Ρ€Π°ΠΌΠΌΠΎΠΉ 211 ΠŸΡ€Π°Π²ΠΈΡ‚Π΅Π»ΡŒΡΡ‚Π²Π° Российской Π€Π΅Π΄Π΅Ρ€Π°Ρ†ΠΈΠΈ, ΠΊΠΎΠ½Ρ‚Ρ€Π°ΠΊΡ‚ β„– 02.A03.21.0006.The article has been prepared with the support of the Act 211 of the Government of the Russian Federation, the contract No. 02.A03.21.0006

    Applying Financial Information to Manage Corporate Risks from the COVID‑19 Pandemic

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    The COVID-19 pandemic has had a significant impact on the economy at all levels, from global markets to micro-enterprises. At the same time, the pandemic and its consequences have left a wide digital footprint. Its study seems to be extremely relevant, since approaches to the analysis of the digital footprint of a pandemic and the use of its results for risk management can be successfully applied in the event of similar threats. The relevance of the problem is also recognized by economists who note the significant impact of the pandemic on the economy and economic theory in general. The aim of the study is to develop approaches to the rapid quantitative assessment of the impact of the pandemic on the university based on the data of accounting financial systems, their testing and generation of proposals for minimizing the risks of financial and economic activities. The scientific hypothesis of the study is that based on the analysis of data transmitted to the social insurance fund on the disability of employees, the effectiveness of risk management of financial and economic activities in a pandemic at the university level can be improved. Growth in efficiency is ensured by adjusting plans to minimize risks, taking into account the heterogeneity of the impact of the pandemic on employees depending on age, gender, and belonging to the category of personnel. For data integration and analysis, the authors used Data Science approaches. Using the data of Ural Federal University as an example, the information content of the analyzed data is shown and what management decisions to minimize risks can be made on their basis. An approach to the quantitative analysis of the impact of the pandemic on employees of a legal entity is proposed. The effectiveness of using distance learning to counter the pandemic, the vulnerability to the pandemic of certain categories of employees, the gender structure of disability are demonstrated. The theoretical significance of the work lies in the development of approaches to the use of financial information to improve risk management. The information obtained can be applied in practice, in particular, to clarify the calculation of reserves, improve technical specifications in the development of information systems.ПандСмия COVID-19 ΠΎΠΊΠ°Π·Π°Π»Π° сущСствСнноС влияниС Π½Π° экономику Π½Π° всСх уровнях, ΠΎΡ‚ Π³Π»ΠΎΠ±Π°Π»ΡŒΠ½Ρ‹Ρ… Ρ€Ρ‹Π½ΠΊΠΎΠ² Π΄ΠΎ микропрСдприятий. ΠŸΡ€ΠΈ этом пандСмия ΠΈ Π΅Π΅ послСдствия оставили ΡˆΠΈΡ€ΠΎΠΊΠΈΠΉ Ρ†ΠΈΡ„Ρ€ΠΎΠ²ΠΎΠΉ слСд. Π•Π³ΠΎ ΠΈΠ·ΡƒΡ‡Π΅Π½ΠΈΠ΅ прСдставляСтся Ρ‡Ρ€Π΅Π·Π²Ρ‹Ρ‡Π°ΠΉΠ½ΠΎ Π°ΠΊΡ‚ΡƒΠ°Π»ΡŒΠ½Ρ‹ΠΌ, Ρ‚Π°ΠΊ ΠΊΠ°ΠΊ ΠΏΠΎΠ΄Ρ…ΠΎΠ΄Ρ‹ ΠΊ Π°Π½Π°Π»ΠΈΠ·Ρƒ Ρ†ΠΈΡ„Ρ€ΠΎΠ²ΠΎΠ³ΠΎ слСда ΠΏΠ°Π½Π΄Π΅ΠΌΠΈΠΈ ΠΈ использованиС Π΅Π³ΠΎ Ρ€Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚ΠΎΠ² для управлСния рисками ΠΌΠΎΠ³ΡƒΡ‚ Π±Ρ‹Ρ‚ΡŒ ΡƒΡΠΏΠ΅ΡˆΠ½ΠΎ ΠΏΡ€ΠΈΠΌΠ΅Π½Π΅Π½Ρ‹ Π² случаС возникновСния Π°Π½Π°Π»ΠΎΠ³ΠΈΡ‡Π½Ρ‹Ρ… ΡƒΠ³Ρ€ΠΎΠ·. ΠΠΊΡ‚ΡƒΠ°Π»ΡŒΠ½ΠΎΡΡ‚ΡŒ ΠΏΡ€ΠΎΠ±Π»Π΅ΠΌΡ‹ признаСтся ΠΈ ΡƒΡ‡Π΅Π½Ρ‹ΠΌΠΈ-экономистами, ΠΊΠΎΡ‚ΠΎΡ€Ρ‹Π΅ ΠΎΡ‚ΠΌΠ΅Ρ‡Π°ΡŽΡ‚ сущСствСнноС влияниС ΠΏΠ°Π½Π΄Π΅ΠΌΠΈΠΈ Π½Π° экономику ΠΈ ΡΠΊΠΎΠ½ΠΎΠΌΠΈΡ‡Π΅ΡΠΊΡƒΡŽ Ρ‚Π΅ΠΎΡ€ΠΈΡŽ Π² Ρ†Π΅Π»ΠΎΠΌ. ЦСлью исслСдования являСтся Ρ€Π°Π·Ρ€Π°Π±ΠΎΡ‚ΠΊΠ° ΠΏΠΎΠ΄Ρ…ΠΎΠ΄ΠΎΠ² ΠΊ ΠΎΠΏΠ΅Ρ€Π°Ρ‚ΠΈΠ²Π½ΠΎΠΉ количСствСнной ΠΎΡ†Π΅Π½ΠΊΠ΅ влияния ΠΏΠ°Π½Π΄Π΅ΠΌΠΈΠΈ Π½Π° унивСрситСт Π½Π° основС Π΄Π°Π½Π½Ρ‹Ρ… ΡƒΡ‡Π΅Ρ‚Π½Ρ‹Ρ… финансовых систСм, ΠΈΡ… апробация ΠΈ гСнСрация ΠΏΡ€Π΅Π΄Π»ΠΎΠΆΠ΅Π½ΠΈΠΉ ΠΏΠΎ ΠΌΠΈΠ½ΠΈΠΌΠΈΠ·Π°Ρ†ΠΈΠΈ рисков финансово-хозяйствСнной Π΄Π΅ΡΡ‚Π΅Π»ΡŒΠ½ΠΎΡΡ‚ΠΈ. Научная Π³ΠΈΠΏΠΎΡ‚Π΅Π·Π° исслСдования состоит Π² Ρ‚ΠΎΠΌ, Ρ‡Ρ‚ΠΎ Π½Π° основС Π°Π½Π°Π»ΠΈΠ·Π° Π΄Π°Π½Π½Ρ‹Ρ…, ΠΏΠ΅Ρ€Π΅Π΄Π°Π²Π°Π΅ΠΌΡ‹Ρ… Ρ„ΠΎΠ½Π΄Ρƒ ΡΠΎΡ†ΠΈΠ°Π»ΡŒΠ½ΠΎΠ³ΠΎ страхования, ΠΎ нСтрудоспособности сотрудников ΠΌΠΎΠΆΠ΅Ρ‚ Π±Ρ‹Ρ‚ΡŒ ΠΏΠΎΠ²Ρ‹ΡˆΠ΅Π½Π° ΡΡ„Ρ„Π΅ΠΊΡ‚ΠΈΠ²Π½ΠΎΡΡ‚ΡŒ управлСния рисками финансово-хозяйствСнной Π΄Π΅ΡΡ‚Π΅Π»ΡŒΠ½ΠΎΡΡ‚ΠΈ Π² условиях ΠΏΠ°Π½Π΄Π΅ΠΌΠΈΠΈ Π½Π° ΡƒΡ€ΠΎΠ²Π½Π΅ унивСрситСта. Рост эффСктивности обСспСчиваСтся ΠΊΠΎΡ€Ρ€Π΅ΠΊΡ†ΠΈΠ΅ΠΉ ΠΏΠ»Π°Π½ΠΎΠ² ΠΏΠΎ ΠΌΠΈΠ½ΠΈΠΌΠΈΠ·Π°Ρ†ΠΈΠΈ рисков с ΡƒΡ‡Π΅Ρ‚ΠΎΠΌ нСоднородности влияния ΠΏΠ°Π½Π΄Π΅ΠΌΠΈΠΈ Π½Π° сотрудников Π² зависимости ΠΎΡ‚ возраста, Π³Π΅Π½Π΄Π΅Ρ€Π°, принадлСТности ΠΊ ΠΊΠ°Ρ‚Π΅Π³ΠΎΡ€ΠΈΠΈ пСрсонала. Для ΠΈΠ½Ρ‚Π΅Π³Ρ€Π°Ρ†ΠΈΠΈ ΠΈ Π°Π½Π°Π»ΠΈΠ·Π° Π΄Π°Π½Π½Ρ‹Ρ… Π°Π²Ρ‚ΠΎΡ€Π°ΠΌΠΈ использовались ΠΏΠΎΠ΄Ρ…ΠΎΠ΄Ρ‹ Data Science. На ΠΏΡ€ΠΈΠΌΠ΅Ρ€Π΅ Π΄Π°Π½Π½Ρ‹Ρ… Π£Ρ€Π°Π»ΡŒΡΠΊΠΎΠ³ΠΎ Ρ„Π΅Π΄Π΅Ρ€Π°Π»ΡŒΠ½ΠΎΠ³ΠΎ унивСрситСта ΠΏΠΎΠΊΠ°Π·Π°Π½Π° ΠΈΠ½Ρ„ΠΎΡ€ΠΌΠ°Ρ‚ΠΈΠ²Π½ΠΎΡΡ‚ΡŒ Π°Π½Π°Π»ΠΈΠ·ΠΈΡ€ΡƒΠ΅ΠΌΡ‹Ρ… Π΄Π°Π½Π½Ρ‹Ρ… ΠΈ ΠΊΠ°ΠΊΠΈΠ΅ управлСнчСскиС Ρ€Π΅ΡˆΠ΅Π½ΠΈΡ ΠΏΠΎ ΠΌΠΈΠ½ΠΈΠΌΠΈΠ·Π°Ρ†ΠΈΠΈ рисков ΠΌΠΎΠ³ΡƒΡ‚ Π±Ρ‹Ρ‚ΡŒ приняты Π½Π° ΠΈΡ… основС. ΠŸΡ€Π΅Π΄Π»ΠΎΠΆΠ΅Π½ ΠΏΠΎΠ΄Ρ…ΠΎΠ΄ ΠΊ количСствСнному Π°Π½Π°Π»ΠΈΠ·Ρƒ воздСйствия ΠΏΠ°Π½Π΄Π΅ΠΌΠΈΠΈ Π½Π° сотрудников ΡŽΡ€ΠΈΠ΄ΠΈΡ‡Π΅ΡΠΊΠΎΠ³ΠΎ Π»ΠΈΡ†Π°. ΠŸΡ€ΠΎΠ΄Π΅ΠΌΠΎΠ½ΡΡ‚Ρ€ΠΈΡ€ΠΎΠ²Π°Π½Π° ΡΡ„Ρ„Π΅ΠΊΡ‚ΠΈΠ²Π½ΠΎΡΡ‚ΡŒ примСнСния дистанционного обучСния для противодСйствия ΠΏΠ°Π½Π΄Π΅ΠΌΠΈΠΈ, ΡƒΡΠ·Π²ΠΈΠΌΠΎΡΡ‚ΡŒ для ΠΏΠ°Π½Π΄Π΅ΠΌΠΈΠΈ ΠΎΡ‚Π΄Π΅Π»ΡŒΠ½Ρ‹Ρ… ΠΊΠ°Ρ‚Π΅Π³ΠΎΡ€ΠΈΠΉ сотрудников, гСндСрная структура нСтрудоспособности. ВСорСтичСская Π·Π½Π°Ρ‡ΠΈΠΌΠΎΡΡ‚ΡŒ Ρ€Π°Π±ΠΎΡ‚Ρ‹ состоит Π² Ρ€Π°Π·Ρ€Π°Π±ΠΎΡ‚ΠΊΠ΅ ΠΏΠΎΠ΄Ρ…ΠΎΠ΄ΠΎΠ² ΠΊ использованию финансовой ΠΈΠ½Ρ„ΠΎΡ€ΠΌΠ°Ρ†ΠΈΠΈ для ΡΠΎΠ²Π΅Ρ€ΡˆΠ΅Π½ΡΡ‚Π²ΠΎΠ²Π°Π½ΠΈΡ управлСния рисками. ΠŸΠΎΠ»ΡƒΡ‡Π΅Π½Π½Π°Ρ информация ΠΌΠΎΠΆΠ΅Ρ‚ Π±Ρ‹Ρ‚ΡŒ ΠΏΡ€ΠΈΠΌΠ΅Π½Π΅Π½Π° Π½Π° ΠΏΡ€Π°ΠΊΡ‚ΠΈΠΊΠ΅, Π² частности, для уточнСния расчСта Ρ€Π΅Π·Π΅Ρ€Π²ΠΎΠ², ΡΠΎΠ²Π΅Ρ€ΡˆΠ΅Π½ΡΡ‚Π²ΠΎΠ²Π°Π½ΠΈΠ΅ тСхничСских Π·Π°Π΄Π°Π½ΠΈΠΉ ΠΏΡ€ΠΈ Ρ€Π°Π·Ρ€Π°Π±ΠΎΡ‚ΠΊΠ΅ ΠΈΠ½Ρ„ΠΎΡ€ΠΌΠ°Ρ†ΠΈΠΎΠ½Π½Ρ‹Ρ… систСм.ПандСмия COVID-19 ΠΎΠΊΠ°Π·Π°Π»Π° сущСствСнноС влияниС Π½Π° экономику Π½Π° всСх уровнях, ΠΎΡ‚ Π³Π»ΠΎΠ±Π°Π»ΡŒΠ½Ρ‹Ρ… Ρ€Ρ‹Π½ΠΊΠΎΠ² Π΄ΠΎ микропрСдприятий. ΠŸΡ€ΠΈ этом пандСмия ΠΈ Π΅Π΅ послСдствия оставили ΡˆΠΈΡ€ΠΎΠΊΠΈΠΉ Ρ†ΠΈΡ„Ρ€ΠΎΠ²ΠΎΠΉ слСд. Π•Π³ΠΎ ΠΈΠ·ΡƒΡ‡Π΅Π½ΠΈΠ΅ прСдставляСтся Ρ‡Ρ€Π΅Π·Π²Ρ‹Ρ‡Π°ΠΉΠ½ΠΎ Π°ΠΊΡ‚ΡƒΠ°Π»ΡŒΠ½Ρ‹ΠΌ, Ρ‚Π°ΠΊ ΠΊΠ°ΠΊ ΠΏΠΎΠ΄Ρ…ΠΎΠ΄Ρ‹ ΠΊ Π°Π½Π°Π»ΠΈΠ·Ρƒ Ρ†ΠΈΡ„Ρ€ΠΎΠ²ΠΎΠ³ΠΎ слСда ΠΏΠ°Π½Π΄Π΅ΠΌΠΈΠΈ ΠΈ использованиС Π΅Π³ΠΎ Ρ€Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚ΠΎΠ² для управлСния рисками ΠΌΠΎΠ³ΡƒΡ‚ Π±Ρ‹Ρ‚ΡŒ ΡƒΡΠΏΠ΅ΡˆΠ½ΠΎ ΠΏΡ€ΠΈΠΌΠ΅Π½Π΅Π½Ρ‹ Π² случаС возникновСния Π°Π½Π°Π»ΠΎΠ³ΠΈΡ‡Π½Ρ‹Ρ… ΡƒΠ³Ρ€ΠΎΠ·. ΠΠΊΡ‚ΡƒΠ°Π»ΡŒΠ½ΠΎΡΡ‚ΡŒ ΠΏΡ€ΠΎΠ±Π»Π΅ΠΌΡ‹ признаСтся ΠΈ ΡƒΡ‡Π΅Π½Ρ‹ΠΌΠΈ-экономистами, ΠΊΠΎΡ‚ΠΎΡ€Ρ‹Π΅ ΠΎΡ‚ΠΌΠ΅Ρ‡Π°ΡŽΡ‚ сущСствСнноС влияниС ΠΏΠ°Π½Π΄Π΅ΠΌΠΈΠΈ Π½Π° экономику ΠΈ ΡΠΊΠΎΠ½ΠΎΠΌΠΈΡ‡Π΅ΡΠΊΡƒΡŽ Ρ‚Π΅ΠΎΡ€ΠΈΡŽ Π² Ρ†Π΅Π»ΠΎΠΌ. ЦСлью исслСдования являСтся Ρ€Π°Π·Ρ€Π°Π±ΠΎΡ‚ΠΊΠ° ΠΏΠΎΠ΄Ρ…ΠΎΠ΄ΠΎΠ² ΠΊ ΠΎΠΏΠ΅Ρ€Π°Ρ‚ΠΈΠ²Π½ΠΎΠΉ количСствСнной ΠΎΡ†Π΅Π½ΠΊΠ΅ влияния ΠΏΠ°Π½Π΄Π΅ΠΌΠΈΠΈ Π½Π° унивСрситСт Π½Π° основС Π΄Π°Π½Π½Ρ‹Ρ… ΡƒΡ‡Π΅Ρ‚Π½Ρ‹Ρ… финансовых систСм, ΠΈΡ… апробация ΠΈ гСнСрация ΠΏΡ€Π΅Π΄Π»ΠΎΠΆΠ΅Π½ΠΈΠΉ ΠΏΠΎ ΠΌΠΈΠ½ΠΈΠΌΠΈΠ·Π°Ρ†ΠΈΠΈ рисков финансово-хозяйствСнной Π΄Π΅ΡΡ‚Π΅Π»ΡŒΠ½ΠΎΡΡ‚ΠΈ. Научная Π³ΠΈΠΏΠΎΡ‚Π΅Π·Π° исслСдования состоит Π² Ρ‚ΠΎΠΌ, Ρ‡Ρ‚ΠΎ Π½Π° основС Π°Π½Π°Π»ΠΈΠ·Π° Π΄Π°Π½Π½Ρ‹Ρ…, ΠΏΠ΅Ρ€Π΅Π΄Π°Π²Π°Π΅ΠΌΡ‹Ρ… Ρ„ΠΎΠ½Π΄Ρƒ ΡΠΎΡ†ΠΈΠ°Π»ΡŒΠ½ΠΎΠ³ΠΎ страхования, ΠΎ нСтрудоспособности сотрудников ΠΌΠΎΠΆΠ΅Ρ‚ Π±Ρ‹Ρ‚ΡŒ ΠΏΠΎΠ²Ρ‹ΡˆΠ΅Π½Π° ΡΡ„Ρ„Π΅ΠΊΡ‚ΠΈΠ²Π½ΠΎΡΡ‚ΡŒ управлСния рисками финансово-хозяйствСнной Π΄Π΅ΡΡ‚Π΅Π»ΡŒΠ½ΠΎΡΡ‚ΠΈ Π² условиях ΠΏΠ°Π½Π΄Π΅ΠΌΠΈΠΈ Π½Π° ΡƒΡ€ΠΎΠ²Π½Π΅ унивСрситСта. Рост эффСктивности обСспСчиваСтся ΠΊΠΎΡ€Ρ€Π΅ΠΊΡ†ΠΈΠ΅ΠΉ ΠΏΠ»Π°Π½ΠΎΠ² ΠΏΠΎ ΠΌΠΈΠ½ΠΈΠΌΠΈΠ·Π°Ρ†ΠΈΠΈ рисков с ΡƒΡ‡Π΅Ρ‚ΠΎΠΌ нСоднородности влияния ΠΏΠ°Π½Π΄Π΅ΠΌΠΈΠΈ Π½Π° сотрудников Π² зависимости ΠΎΡ‚ возраста, Π³Π΅Π½Π΄Π΅Ρ€Π°, принадлСТности ΠΊ ΠΊΠ°Ρ‚Π΅Π³ΠΎΡ€ΠΈΠΈ пСрсонала. Для ΠΈΠ½Ρ‚Π΅Π³Ρ€Π°Ρ†ΠΈΠΈ ΠΈ Π°Π½Π°Π»ΠΈΠ·Π° Π΄Π°Π½Π½Ρ‹Ρ… Π°Π²Ρ‚ΠΎΡ€Π°ΠΌΠΈ использовались ΠΏΠΎΠ΄Ρ…ΠΎΠ΄Ρ‹ Data Science. На ΠΏΡ€ΠΈΠΌΠ΅Ρ€Π΅ Π΄Π°Π½Π½Ρ‹Ρ… Π£Ρ€Π°Π»ΡŒΡΠΊΠΎΠ³ΠΎ Ρ„Π΅Π΄Π΅Ρ€Π°Π»ΡŒΠ½ΠΎΠ³ΠΎ унивСрситСта ΠΏΠΎΠΊΠ°Π·Π°Π½Π° ΠΈΠ½Ρ„ΠΎΡ€ΠΌΠ°Ρ‚ΠΈΠ²Π½ΠΎΡΡ‚ΡŒ Π°Π½Π°Π»ΠΈΠ·ΠΈΡ€ΡƒΠ΅ΠΌΡ‹Ρ… Π΄Π°Π½Π½Ρ‹Ρ… ΠΈ ΠΊΠ°ΠΊΠΈΠ΅ управлСнчСскиС Ρ€Π΅ΡˆΠ΅Π½ΠΈΡ ΠΏΠΎ ΠΌΠΈΠ½ΠΈΠΌΠΈΠ·Π°Ρ†ΠΈΠΈ рисков ΠΌΠΎΠ³ΡƒΡ‚ Π±Ρ‹Ρ‚ΡŒ приняты Π½Π° ΠΈΡ… основС. ΠŸΡ€Π΅Π΄Π»ΠΎΠΆΠ΅Π½ ΠΏΠΎΠ΄Ρ…ΠΎΠ΄ ΠΊ количСствСнному Π°Π½Π°Π»ΠΈΠ·Ρƒ воздСйствия ΠΏΠ°Π½Π΄Π΅ΠΌΠΈΠΈ Π½Π° сотрудников ΡŽΡ€ΠΈΠ΄ΠΈΡ‡Π΅ΡΠΊΠΎΠ³ΠΎ Π»ΠΈΡ†Π°. ΠŸΡ€ΠΎΠ΄Π΅ΠΌΠΎΠ½ΡΡ‚Ρ€ΠΈΡ€ΠΎΠ²Π°Π½Π° ΡΡ„Ρ„Π΅ΠΊΡ‚ΠΈΠ²Π½ΠΎΡΡ‚ΡŒ примСнСния дистанционного обучСния для противодСйствия ΠΏΠ°Π½Π΄Π΅ΠΌΠΈΠΈ, ΡƒΡΠ·Π²ΠΈΠΌΠΎΡΡ‚ΡŒ для ΠΏΠ°Π½Π΄Π΅ΠΌΠΈΠΈ ΠΎΡ‚Π΄Π΅Π»ΡŒΠ½Ρ‹Ρ… ΠΊΠ°Ρ‚Π΅Π³ΠΎΡ€ΠΈΠΉ сотрудников, гСндСрная структура нСтрудоспособности. ВСорСтичСская Π·Π½Π°Ρ‡ΠΈΠΌΠΎΡΡ‚ΡŒ Ρ€Π°Π±ΠΎΡ‚Ρ‹ состоит Π² Ρ€Π°Π·Ρ€Π°Π±ΠΎΡ‚ΠΊΠ΅ ΠΏΠΎΠ΄Ρ…ΠΎΠ΄ΠΎΠ² ΠΊ использованию финансовой ΠΈΠ½Ρ„ΠΎΡ€ΠΌΠ°Ρ†ΠΈΠΈ для ΡΠΎΠ²Π΅Ρ€ΡˆΠ΅Π½ΡΡ‚Π²ΠΎΠ²Π°Π½ΠΈΡ управлСния рисками. ΠŸΠΎΠ»ΡƒΡ‡Π΅Π½Π½Π°Ρ информация ΠΌΠΎΠΆΠ΅Ρ‚ Π±Ρ‹Ρ‚ΡŒ ΠΏΡ€ΠΈΠΌΠ΅Π½Π΅Π½Π° Π½Π° ΠΏΡ€Π°ΠΊΡ‚ΠΈΠΊΠ΅, Π² частности, для уточнСния расчСта Ρ€Π΅Π·Π΅Ρ€Π²ΠΎΠ², ΡΠΎΠ²Π΅Ρ€ΡˆΠ΅Π½ΡΡ‚Π²ΠΎΠ²Π°Π½ΠΈΠ΅ тСхничСских Π·Π°Π΄Π°Π½ΠΈΠΉ ΠΏΡ€ΠΈ Ρ€Π°Π·Ρ€Π°Π±ΠΎΡ‚ΠΊΠ΅ ΠΈΠ½Ρ„ΠΎΡ€ΠΌΠ°Ρ†ΠΈΠΎΠ½Π½Ρ‹Ρ… систСм
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