61 research outputs found
ΠΠΎΠ΄ ΠΏΠΎΡΠ»Π΅ Π²ΡΠΏΡΡΠΊΠΈ COVID-19: Π²ΠΎΡΠΏΡΠΈΡΡΠΈΠ΅ ΠΏΠΎΡΠ΅Π½ΡΠΈΠ°Π»ΡΠ½ΡΠΌΠΈ ΡΡΡΠ΄Π΅Π½ΡΠ°ΠΌΠΈ ΠΊΠ°ΡΠ΅ΡΡΠ²Π° Π²ΡΡΡΠ΅Π³ΠΎ ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΡ Π² ΠΊΠΎΠ½ΡΠ΅ΠΊΡΡΠ΅ ΡΠΈΡΡΠΎΠ²ΠΈΠ·Π°ΡΠΈΠΈ ΠΈ ΡΠΌΠ΅ΡΠ°Π½Π½ΠΎΠ³ΠΎ ΠΎΠ±ΡΡΠ΅Π½ΠΈΡ
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 applicantsβ positive attitude to learning that includes online elements in the context of the Russian universitiesβ 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 bachelorβs 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 masterβs degree upon completing their bachelorβs degree course; those who choose their degree field rationally β men who apply for a state-funded education in any Russian university (including participants of federal contests β βAcademic Olympicsβ). The above groups are formed mainly under the influence of external factors. Another group includes those oriented towards self-realization β 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 universitiesβ 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
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
Π£Π½ΠΈΠ²Π΅ΡΡΠΈΡΠ΅ΡΡΠΊΠΈΠΉ ΠΈ ΡΠ΅Π³ΠΈΠΎΠ½Π°Π»ΡΠ½ΡΠΉ Π»Π°Π½Π΄ΡΠ°ΡΡ ΡΠΎΡΡΠΈΠΉΡΠΊΠΎΠΉ Π°ΡΠΏΠΈΡΠ°Π½ΡΡΡΡ, ΡΠΈΠ½Π°Π½ΡΠΎΠ²ΡΠ΅ ΡΡΠ°Π΅ΠΊΡΠΎΡΠΈΠΈ ΠΎΠ±ΡΡΠ°ΡΡΠΈΡ ΡΡ
ΠΠ΅Π΄ΡΡΡΡ ΡΠΎΠ»Ρ Π² ΠΎΠ±Π΅ΡΠΏΠ΅ΡΠ΅Π½ΠΈΠΈ ΡΡΡΠΎΠΉΡΠΈΠ²ΠΎΠ³ΠΎ ΡΠΎΡΠΈΠ°Π»ΡΠ½ΠΎ-ΡΠΊΠΎΠ½ΠΎΠΌΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΡΠ°Π·Π²ΠΈΡΠΈΡ ΡΡΡΠ°Π½Ρ ΠΈ Π°ΠΊΡΠΈΠ²Π½ΠΎΠ³ΠΎ Π½Π°ΡΠ°ΡΠΈΠ²Π°Π½ΠΈΡ ΡΠ΅Π»ΠΎΠ²Π΅ΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΊΠ°ΠΏΠΈΡΠ°Π»Π° ΠΏΡΡΠ΅ΠΌ Π½Π΅ΠΏΡΠ΅ΡΡΠ²Π½ΠΎΠ³ΠΎ Π²ΠΎΡΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄ΡΡΠ²Π° ΠΏΠ΅ΡΠ΅Π΄ΠΎΠ²ΠΎΠ³ΠΎ Π·Π½Π°Π½ΠΈΡ Π² ΡΠΎΡΠΌΠ΅ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠΉ ΠΈ ΡΠ°Π·ΡΠ°Π±ΠΎΡΠΎΠΊ Π΄Π»Ρ ΠΏΡΠΎΠΌΡΡΠ»Π΅Π½Π½ΠΎΡΡΠΈ ΠΈΠ³ΡΠ°Π΅Ρ ΠΏΠΎΠ΄Π³ΠΎΡΠΎΠ²ΠΊΠ° Π½ΠΎΠ²ΠΎΠ³ΠΎ ΠΏΠΎΠΊΠΎΠ»Π΅Π½ΠΈΡ Π°ΡΠΏΠΈΡΠ°Π½ΡΠΎΠ². ΠΠΊΡΡΠ°Π»ΡΠ½ΡΠΌΠΈ ΡΠ²Π»ΡΡΡΡΡ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°ΡΠ΅Π»ΡΡΠΊΠΈΠ΅ Π²ΠΎΠΏΡΠΎΡΡ: Π² ΡΠΈΡΠΎΠΊΠΎΠΌ ΠΊΠΎΠ½ΡΠ΅ΠΊΡΡΠ΅ β ΠΊΠ°ΠΊΠΎΠ²Π° Π΄ΠΈΠ½Π°ΠΌΠΈΠΊΠ° ΡΠ°Π·Π²ΠΈΡΠΈΡ ΡΠΎΡΡΠΈΠΉΡΠΊΠΎΠΉ Π°ΡΠΏΠΈΡΠ°Π½ΡΡΡΡ Π² ΡΠ΅Π³ΠΈΠΎΠ½Π°Ρ
ΠΈ, Π² ΡΠ°ΡΡΠ½ΠΎΡΡΠΈ, β Π½Π°ΡΠΊΠΎΠ»ΡΠΊΠΎ Π±Π»Π°Π³ΠΎΠΏΠΎΠ»ΡΡΠ½Ρ Π°ΡΠΏΠΈΡΠ°Π½ΡΡ, ΡΡΠΎΠ±Ρ ΡΡΠΏΠ΅ΡΠ½ΠΎ ΡΠ΅Π°Π»ΠΈΠ·ΠΎΠ²ΡΠ²Π°ΡΡ ΡΠ²ΠΎΠΈ ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°ΡΠ΅Π»ΡΠ½ΡΠ΅ ΡΡΠ°Π΅ΠΊΡΠΎΡΠΈΠΈ (ΡΡΡΠ΄ΠΎΡΡΡΡΠΎΠ΅Π½Ρ Π»ΠΈ, ΠΈΠΌΠ΅ΡΡ Π»ΠΈ Π² ΡΡΠΎΠΌ ΠΏΠΎΡΡΠ΅Π±Π½ΠΎΡΡΡ) ΠΈ ΠΊΠ°ΠΊΠΎΠ²Π° ΡΠΏΠ΅ΡΠΈΡΠΈΠΊΠ° ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°ΡΠ΅Π»ΡΠ½ΠΎΠΉ ΠΌΠΎΠ±ΠΈΠ»ΡΠ½ΠΎΡΡΠΈ Ρ ΡΠ΅Π»ΡΡ ΠΏΠΎΠ»ΡΡΠ΅Π½ΠΈΡ Π²ΡΡΡΠ΅ΠΉ ΠΊΠ²Π°Π»ΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ. ΠΠ°ΡΡΠ½ΡΠΉ ΠΈΠ½ΡΠ΅ΡΠ΅Ρ ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»ΡΠ΅Ρ ΠΈΠ·ΡΡΠ΅Π½ΠΈΠ΅ ΡΠ°Π·Π²ΠΈΡΠΈΡ Π°ΡΠΏΠΈΡΠ°Π½ΡΡΡΡ Ρ ΡΡΠ΅ΡΠΎΠΌ ΡΠ°ΡΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΡ ΡΠ΅ΡΡΡΡΠΎΠ² (ΡΠΈΠ½Π°Π½ΡΠΎΠ²ΡΡ
, ΠΈΠ½ΡΠ΅Π»Π»Π΅ΠΊΡΡΠ°Π»ΡΠ½ΡΡ
) ΠΏΠΎ ΡΠ΅Π³ΠΈΠΎΠ½Π°ΠΌ, Π° ΡΠ°ΠΊΠΆΠ΅ ΠΈΡ
ΠΊΠΎΠ½ΡΠ΅Π½ΡΡΠ°ΡΠΈΠΈ Π² Π²ΡΠ·Π°Ρ
, ΡΠΏΠΎΡΠΎΠ±Π½ΡΡ
Π½Π° Π³Π΅Π½Π΅ΡΠ°ΡΠΈΡ ΠΏΡΠΎΡΡΠ²Π½ΡΡ
ΠΈΠ΄Π΅ΠΉ, ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΠΉ Π² ΡΠΎΡΡΠΈΠΉΡΠΊΠΎΠΉ Π½Π°ΡΠΊΠ΅. ΠΠ½ΠΈ Π²ΡΡΡΡΠΏΠ°ΡΡ ΡΠ΅Π½ΡΡΠ°ΠΌΠΈ ΠΏΡΠΈΡΡΠΆΠ΅Π½ΠΈΡ ΠΏΡΠΎΠ°ΠΊΡΠΈΠ²Π½ΠΎΠΉ ΠΌΠΎΠ»ΠΎΠ΄Π΅ΠΆΠΈ Π² ΡΠ΅Π³ΠΈΠΎΠ½. ΠΠ»Ρ ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠΈ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½Ρ ΠΌΠ΅ΡΠΎΠ΄Ρ Π²ΡΠΎΡΠΈΡΠ½ΠΎΠ³ΠΎ Π°Π½Π°Π»ΠΈΠ·Π° Π΄Π°Π½Π½ΡΡ
, ΡΠΎΠΏΠΎΡΡΠ°Π²Π»Π΅Π½ΠΈΡ, ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ, ΠΈΠ½ΡΠΎΠ³ΡΠ°ΡΠΈΠΊΠΈ. ΠΡΠΎΠ°Π½Π°Π»ΠΈΠ·ΠΈΡΠΎΠ²Π°Π½Ρ Π±Π°Π·Ρ ΠΌΠΎΠ½ΠΈΡΠΎΡΠΈΠ½Π³Π° ΡΡΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎΡΡΠΈ Π²ΡΠ·ΠΎΠ² Π Π€ 2014β2020 Π³Π³., ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΡ ΡΠΎΡΠΈΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠΉ Π°ΡΠΏΠΈΡΠ°Π½ΡΠΎΠ² 2017β2020 Π³Π³. Π²Π΅Π΄ΡΡΠ΅Π³ΠΎ ΡΠΎΡΡΠΈΠΉΡΠΊΠΎΠ³ΠΎ Π²ΡΠ·Π°. ΠΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½Π° ΠΊΠ°ΡΡΠ° ΠΊΠΎΠ½ΡΠ΅Π½ΡΡΠ°ΡΠΈΠΈ Π°ΡΠΏΠΈΡΠ°Π½ΡΠΎΠ² Π² ΠΎΡΠ΄Π΅Π»ΡΠ½ΡΡ
ΡΠ΅Π³ΠΈΠΎΠ½Π°Ρ
Ρ ΡΡΠ΅ΡΠΎΠΌ ΡΠΈΡΠ»Π΅Π½Π½ΠΎΡΡΠΈ ΠΈ ΠΏΡΠΈΡΠΎΠΊΠ° / ΠΎΡΡΠΎΠΊΠ°. Π¦Π΅Π½ΡΡΠ°ΠΌΠΈ ΠΏΡΠΈΡΡΠΆΠ΅Π½ΠΈΡ Π°ΡΠΏΠΈΡΠ°Π½ΡΠΎΠ², ΠΏΠΎΠΌΠΈΠΌΠΎ ΡΡΠΎΠ»ΠΈΡΠ½ΡΡ
ΡΠ΅Π³ΠΈΠΎΠ½ΠΎΠ² (ΠΈΡ
Π΄ΠΎΠ»Ρ Π² ΠΎΠ±ΡΠ΅ΠΉ ΡΠΈΡΠ»Π΅Π½Π½ΠΎΡΡΠΈ β 47,9 %), ΡΠ²Π»ΡΡΡΡΡ Π Π΅ΡΠΏΡΠ±Π»ΠΈΠΊΠ° Π’Π°ΡΠ°ΡΡΡΠ°Π½ (3,2 %), Π’ΠΎΠΌΡΠΊΠ°Ρ ΠΎΠ±Π»Π°ΡΡΡ (2,4 %), Π‘Π²Π΅ΡΠ΄Π»ΠΎΠ²ΡΠΊΠ°Ρ ΠΎΠ±Π»Π°ΡΡΡ (2,1 %), Π³Π΄Π΅ ΡΠ°ΡΠΏΠΎΠ»ΠΎΠΆΠ΅Π½Ρ Π²Π΅Π΄ΡΡΠΈΠ΅ ΡΠΎΡΡΠΈΠΉΡΠΊΠΈΠ΅ Π²ΡΠ·Ρ. Π ΡΠΎΠΏ 7 ΡΠ΅Π³ΠΈΠΎΠ½ΠΎΠ² Π²Ρ
ΠΎΠ΄ΡΡ ΠΠ΅Π»Π³ΠΎΡΠΎΠ΄ΡΠΊΠ°Ρ (Π΄ΠΎΠ»Ρ Π°ΡΠΏΠΈΡΠ°Π½ΡΠΎΠ² β 2,7 %) ΠΈ Π ΠΎΡΡΠΎΠ²ΡΠΊΠ°Ρ ΠΎΠ±Π»Π°ΡΡΠΈ (2,4 %) Ρ ΡΠΈΠ»ΡΠ½ΡΠΌΠΈ Π½Π°ΡΡΠ½ΠΎ-ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°ΡΠ΅Π»ΡΡΠΊΠΈΠΌ ΠΈ ΡΠ΅Π΄Π΅ΡΠ°Π»ΡΠ½ΡΠΌ ΡΠ½ΠΈΠ²Π΅ΡΡΠΈΡΠ΅ΡΠ°ΠΌΠΈ. ΠΠ°ΠΆΠ΅ Π² ΡΠ΅Π³ΠΈΠΎΠ½Π°Ρ
, ΠΏΡΠΈΠ²Π»Π΅ΠΊΠ°ΡΡΠΈΡ
Π±ΠΎΠ»ΡΡΠΎΠ΅ ΠΊΠΎΠ»ΠΈΡΠ΅ΡΡΠ²ΠΎ ΠΎΠ±ΡΡΠ°ΡΡΠΈΡ
ΡΡ, Π½Π°Π±Π»ΡΠ΄Π°Π΅ΡΡΡ ΠΈΡ
Π½Π΅Π΄ΠΎΡΠΈΠ½Π°Π½ΡΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅ Π² ΠΏΡΠΎΡΠ΅ΡΡΠ΅ ΠΎΠ±ΡΡΠ΅Π½ΠΈΡ (85 % ΡΠΎΠ²ΠΌΠ΅ΡΠ°ΡΡ ΡΠ°Π±ΠΎΡΡ ΠΈ ΠΎΠ±ΡΡΠ΅Π½ΠΈΠ΅), ΡΠ°ΡΡΠ΅Ρ ΠΎΡΡΠΎΠΊ Π² Π·Π°ΡΡΠ±Π΅ΠΆΠ½ΡΠ΅ Π²ΡΠ·Ρ. Π Π΅Π·ΡΠ»ΡΡΠ°ΡΡ ΠΌΠΎΠ³ΡΡ Π±ΡΡΡ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½Ρ Π΄Π»Ρ ΡΠΎΠ²Π΅ΡΡΠ΅Π½ΡΡΠ²ΠΎΠ²Π°Π½ΠΈΡ ΠΏΡΠΈΠ½ΡΠΈΠΏΠΎΠ² ΡΠ°Π·Π²ΠΈΡΠΈΡ Π°ΡΠΏΠΈΡΠ°Π½ΡΡΡΡ ΠΈ ΠΌΠ΅Ρ
Π°Π½ΠΈΠ·ΠΌΠΎΠ² ΠΏΠΎΠ΄Π΄Π΅ΡΠΆΠΊΠΈ Π°ΡΠΏΠΈΡΠ°Π½ΡΠΎΠ² Π² Π²Π΅Π΄ΡΡΠΈΡ
ΠΈ ΠΈΠ½ΡΡ
ΡΠ΅Π³ΠΈΠΎΠ½Π°Π»ΡΠ½ΡΡ
Π²ΡΠ·Π°Ρ
Ρ ΡΠ΅Π»ΡΡ ΡΡΡΠΎΠΉΡΠΈΠ²ΠΎΠ³ΠΎ ΡΠ°Π·Π²ΠΈΡΠΈΡ ΡΠ΅ΡΡΠΈΡΠΎΡΠΈΠΉ
Π£Π½ΠΈΠ²Π΅ΡΡΠΈΡΠ΅ΡΡΠΊΠΈΠΉ ΠΈ ΡΠ΅Π³ΠΈΠΎΠ½Π°Π»ΡΠ½ΡΠΉ Π»Π°Π½Π΄ΡΠ°ΡΡ ΡΠΎΡΡΠΈΠΉΡΠΊΠΎΠΉ Π°ΡΠΏΠΈΡΠ°Π½ΡΡΡΡ, ΡΠΈΠ½Π°Π½ΡΠΎΠ²ΡΠ΅ ΡΡΠ°Π΅ΠΊΡΠΎΡΠΈΠΈ ΠΎΠ±ΡΡΠ°ΡΡΠΈΡ ΡΡ
ΠΠ΅Π΄ΡΡΡΡ ΡΠΎΠ»Ρ Π² ΠΎΠ±Π΅ΡΠΏΠ΅ΡΠ΅Π½ΠΈΠΈ ΡΡΡΠΎΠΉΡΠΈΠ²ΠΎΠ³ΠΎ ΡΠΎΡΠΈΠ°Π»ΡΠ½ΠΎ-ΡΠΊΠΎΠ½ΠΎΠΌΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΡΠ°Π·Π²ΠΈΡΠΈΡ ΡΡΡΠ°Π½Ρ ΠΈ Π°ΠΊΡΠΈΠ²Π½ΠΎΠ³ΠΎ Π½Π°ΡΠ°ΡΠΈΠ²Π°Π½ΠΈΡ ΡΠ΅Π»ΠΎΠ²Π΅ΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΊΠ°ΠΏΠΈΡΠ°Π»Π° ΠΏΡΡΠ΅ΠΌ Π½Π΅ΠΏΡΠ΅ΡΡΠ²Π½ΠΎΠ³ΠΎ Π²ΠΎΡΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄ΡΡΠ²Π° ΠΏΠ΅ΡΠ΅Π΄ΠΎΠ²ΠΎΠ³ΠΎ Π·Π½Π°Π½ΠΈΡ Π² ΡΠΎΡΠΌΠ΅ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠΉ ΠΈ ΡΠ°Π·ΡΠ°Π±ΠΎΡΠΎΠΊ Π΄Π»Ρ ΠΏΡΠΎΠΌΡΡΠ»Π΅Π½Π½ΠΎΡΡΠΈ ΠΈΠ³ΡΠ°Π΅Ρ ΠΏΠΎΠ΄Π³ΠΎΡΠΎΠ²ΠΊΠ° Π½ΠΎΠ²ΠΎΠ³ΠΎ ΠΏΠΎΠΊΠΎΠ»Π΅Π½ΠΈΡ Π°ΡΠΏΠΈΡΠ°Π½ΡΠΎΠ². ΠΠΊΡΡΠ°Π»ΡΠ½ΡΠΌΠΈ ΡΠ²Π»ΡΡΡΡΡ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°ΡΠ΅Π»ΡΡΠΊΠΈΠ΅ Π²ΠΎΠΏΡΠΎΡΡ: Π² ΡΠΈΡΠΎΠΊΠΎΠΌ ΠΊΠΎΠ½ΡΠ΅ΠΊΡΡΠ΅ β ΠΊΠ°ΠΊΠΎΠ²Π° Π΄ΠΈΠ½Π°ΠΌΠΈΠΊΠ° ΡΠ°Π·Π²ΠΈΡΠΈΡ ΡΠΎΡΡΠΈΠΉΡΠΊΠΎΠΉ Π°ΡΠΏΠΈΡΠ°Π½ΡΡΡΡ Π² ΡΠ΅Π³ΠΈΠΎΠ½Π°Ρ
ΠΈ, Π² ΡΠ°ΡΡΠ½ΠΎΡΡΠΈ, β Π½Π°ΡΠΊΠΎΠ»ΡΠΊΠΎ Π±Π»Π°Π³ΠΎΠΏΠΎΠ»ΡΡΠ½Ρ Π°ΡΠΏΠΈΡΠ°Π½ΡΡ, ΡΡΠΎΠ±Ρ ΡΡΠΏΠ΅ΡΠ½ΠΎ ΡΠ΅Π°Π»ΠΈΠ·ΠΎΠ²ΡΠ²Π°ΡΡ ΡΠ²ΠΎΠΈ ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°ΡΠ΅Π»ΡΠ½ΡΠ΅ ΡΡΠ°Π΅ΠΊΡΠΎΡΠΈΠΈ (ΡΡΡΠ΄ΠΎΡΡΡΡΠΎΠ΅Π½Ρ Π»ΠΈ, ΠΈΠΌΠ΅ΡΡ Π»ΠΈ Π² ΡΡΠΎΠΌ ΠΏΠΎΡΡΠ΅Π±Π½ΠΎΡΡΡ) ΠΈ ΠΊΠ°ΠΊΠΎΠ²Π° ΡΠΏΠ΅ΡΠΈΡΠΈΠΊΠ° ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°ΡΠ΅Π»ΡΠ½ΠΎΠΉ ΠΌΠΎΠ±ΠΈΠ»ΡΠ½ΠΎΡΡΠΈ Ρ ΡΠ΅Π»ΡΡ ΠΏΠΎΠ»ΡΡΠ΅Π½ΠΈΡ Π²ΡΡΡΠ΅ΠΉ ΠΊΠ²Π°Π»ΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ. ΠΠ°ΡΡΠ½ΡΠΉ ΠΈΠ½ΡΠ΅ΡΠ΅Ρ ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»ΡΠ΅Ρ ΠΈΠ·ΡΡΠ΅Π½ΠΈΠ΅ ΡΠ°Π·Π²ΠΈΡΠΈΡ Π°ΡΠΏΠΈΡΠ°Π½ΡΡΡΡ Ρ ΡΡΠ΅ΡΠΎΠΌ ΡΠ°ΡΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΡ ΡΠ΅ΡΡΡΡΠΎΠ² (ΡΠΈΠ½Π°Π½ΡΠΎΠ²ΡΡ
, ΠΈΠ½ΡΠ΅Π»Π»Π΅ΠΊΡΡΠ°Π»ΡΠ½ΡΡ
) ΠΏΠΎ ΡΠ΅Π³ΠΈΠΎΠ½Π°ΠΌ, Π° ΡΠ°ΠΊΠΆΠ΅ ΠΈΡ
ΠΊΠΎΠ½ΡΠ΅Π½ΡΡΠ°ΡΠΈΠΈ Π² Π²ΡΠ·Π°Ρ
, ΡΠΏΠΎΡΠΎΠ±Π½ΡΡ
Π½Π° Π³Π΅Π½Π΅ΡΠ°ΡΠΈΡ ΠΏΡΠΎΡΡΠ²Π½ΡΡ
ΠΈΠ΄Π΅ΠΉ, ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΠΉ Π² ΡΠΎΡΡΠΈΠΉΡΠΊΠΎΠΉ Π½Π°ΡΠΊΠ΅. ΠΠ½ΠΈ Π²ΡΡΡΡΠΏΠ°ΡΡ ΡΠ΅Π½ΡΡΠ°ΠΌΠΈ ΠΏΡΠΈΡΡΠΆΠ΅Π½ΠΈΡ ΠΏΡΠΎΠ°ΠΊΡΠΈΠ²Π½ΠΎΠΉ ΠΌΠΎΠ»ΠΎΠ΄Π΅ΠΆΠΈ Π² ΡΠ΅Π³ΠΈΠΎΠ½. ΠΠ»Ρ ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠΈ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½Ρ ΠΌΠ΅ΡΠΎΠ΄Ρ Π²ΡΠΎΡΠΈΡΠ½ΠΎΠ³ΠΎ Π°Π½Π°Π»ΠΈΠ·Π° Π΄Π°Π½Π½ΡΡ
, ΡΠΎΠΏΠΎΡΡΠ°Π²Π»Π΅Π½ΠΈΡ, ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ, ΠΈΠ½ΡΠΎΠ³ΡΠ°ΡΠΈΠΊΠΈ. ΠΡΠΎΠ°Π½Π°Π»ΠΈΠ·ΠΈΡΠΎΠ²Π°Π½Ρ Π±Π°Π·Ρ ΠΌΠΎΠ½ΠΈΡΠΎΡΠΈΠ½Π³Π° ΡΡΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎΡΡΠΈ Π²ΡΠ·ΠΎΠ² Π Π€ 2014β2020 Π³Π³., ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΡ ΡΠΎΡΠΈΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠΉ Π°ΡΠΏΠΈΡΠ°Π½ΡΠΎΠ² 2017β2020 Π³Π³. Π²Π΅Π΄ΡΡΠ΅Π³ΠΎ ΡΠΎΡΡΠΈΠΉΡΠΊΠΎΠ³ΠΎ Π²ΡΠ·Π°. ΠΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½Π° ΠΊΠ°ΡΡΠ° ΠΊΠΎΠ½ΡΠ΅Π½ΡΡΠ°ΡΠΈΠΈ Π°ΡΠΏΠΈΡΠ°Π½ΡΠΎΠ² Π² ΠΎΡΠ΄Π΅Π»ΡΠ½ΡΡ
ΡΠ΅Π³ΠΈΠΎΠ½Π°Ρ
Ρ ΡΡΠ΅ΡΠΎΠΌ ΡΠΈΡΠ»Π΅Π½Π½ΠΎΡΡΠΈ ΠΈ ΠΏΡΠΈΡΠΎΠΊΠ° / ΠΎΡΡΠΎΠΊΠ°. Π¦Π΅Π½ΡΡΠ°ΠΌΠΈ ΠΏΡΠΈΡΡΠΆΠ΅Π½ΠΈΡ Π°ΡΠΏΠΈΡΠ°Π½ΡΠΎΠ², ΠΏΠΎΠΌΠΈΠΌΠΎ ΡΡΠΎΠ»ΠΈΡΠ½ΡΡ
ΡΠ΅Π³ΠΈΠΎΠ½ΠΎΠ² (ΠΈΡ
Π΄ΠΎΠ»Ρ Π² ΠΎΠ±ΡΠ΅ΠΉ ΡΠΈΡΠ»Π΅Π½Π½ΠΎΡΡΠΈ β 47,9 %), ΡΠ²Π»ΡΡΡΡΡ Π Π΅ΡΠΏΡΠ±Π»ΠΈΠΊΠ° Π’Π°ΡΠ°ΡΡΡΠ°Π½ (3,2 %), Π’ΠΎΠΌΡΠΊΠ°Ρ ΠΎΠ±Π»Π°ΡΡΡ (2,4 %), Π‘Π²Π΅ΡΠ΄Π»ΠΎΠ²ΡΠΊΠ°Ρ ΠΎΠ±Π»Π°ΡΡΡ (2,1 %), Π³Π΄Π΅ ΡΠ°ΡΠΏΠΎΠ»ΠΎΠΆΠ΅Π½Ρ Π²Π΅Π΄ΡΡΠΈΠ΅ ΡΠΎΡΡΠΈΠΉΡΠΊΠΈΠ΅ Π²ΡΠ·Ρ. Π ΡΠΎΠΏ 7 ΡΠ΅Π³ΠΈΠΎΠ½ΠΎΠ² Π²Ρ
ΠΎΠ΄ΡΡ ΠΠ΅Π»Π³ΠΎΡΠΎΠ΄ΡΠΊΠ°Ρ (Π΄ΠΎΠ»Ρ Π°ΡΠΏΠΈΡΠ°Π½ΡΠΎΠ² β 2,7 %) ΠΈ Π ΠΎΡΡΠΎΠ²ΡΠΊΠ°Ρ ΠΎΠ±Π»Π°ΡΡΠΈ (2,4 %) Ρ ΡΠΈΠ»ΡΠ½ΡΠΌΠΈ Π½Π°ΡΡΠ½ΠΎ-ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°ΡΠ΅Π»ΡΡΠΊΠΈΠΌ ΠΈ ΡΠ΅Π΄Π΅ΡΠ°Π»ΡΠ½ΡΠΌ ΡΠ½ΠΈΠ²Π΅ΡΡΠΈΡΠ΅ΡΠ°ΠΌΠΈ. ΠΠ°ΠΆΠ΅ Π² ΡΠ΅Π³ΠΈΠΎΠ½Π°Ρ
, ΠΏΡΠΈΠ²Π»Π΅ΠΊΠ°ΡΡΠΈΡ
Π±ΠΎΠ»ΡΡΠΎΠ΅ ΠΊΠΎΠ»ΠΈΡΠ΅ΡΡΠ²ΠΎ ΠΎΠ±ΡΡΠ°ΡΡΠΈΡ
ΡΡ, Π½Π°Π±Π»ΡΠ΄Π°Π΅ΡΡΡ ΠΈΡ
Π½Π΅Π΄ΠΎΡΠΈΠ½Π°Π½ΡΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅ Π² ΠΏΡΠΎΡΠ΅ΡΡΠ΅ ΠΎΠ±ΡΡΠ΅Π½ΠΈΡ (85 % ΡΠΎΠ²ΠΌΠ΅ΡΠ°ΡΡ ΡΠ°Π±ΠΎΡΡ ΠΈ ΠΎΠ±ΡΡΠ΅Π½ΠΈΠ΅), ΡΠ°ΡΡΠ΅Ρ ΠΎΡΡΠΎΠΊ Π² Π·Π°ΡΡΠ±Π΅ΠΆΠ½ΡΠ΅ Π²ΡΠ·Ρ. Π Π΅Π·ΡΠ»ΡΡΠ°ΡΡ ΠΌΠΎΠ³ΡΡ Π±ΡΡΡ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½Ρ Π΄Π»Ρ ΡΠΎΠ²Π΅ΡΡΠ΅Π½ΡΡΠ²ΠΎΠ²Π°Π½ΠΈΡ ΠΏΡΠΈΠ½ΡΠΈΠΏΠΎΠ² ΡΠ°Π·Π²ΠΈΡΠΈΡ Π°ΡΠΏΠΈΡΠ°Π½ΡΡΡΡ ΠΈ ΠΌΠ΅Ρ
Π°Π½ΠΈΠ·ΠΌΠΎΠ² ΠΏΠΎΠ΄Π΄Π΅ΡΠΆΠΊΠΈ Π°ΡΠΏΠΈΡΠ°Π½ΡΠΎΠ² Π² Π²Π΅Π΄ΡΡΠΈΡ
ΠΈ ΠΈΠ½ΡΡ
ΡΠ΅Π³ΠΈΠΎΠ½Π°Π»ΡΠ½ΡΡ
Π²ΡΠ·Π°Ρ
Ρ ΡΠ΅Π»ΡΡ ΡΡΡΠΎΠΉΡΠΈΠ²ΠΎΠ³ΠΎ ΡΠ°Π·Π²ΠΈΡΠΈΡ ΡΠ΅ΡΡΠΈΡΠΎΡΠΈΠΉ
Differentiation of universities by the level of teaching staff income: Correlation with the quality of education and research productivity
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 βsmartβ and βrichβ. The institutions that focus on education can be βrichβ 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
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 βBecton Dickinson FACS Canto 2β 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
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 60β74 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 βMultiscanβ 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
ΠΠ΅Π΄ΡΡΡΡ ΡΠΎΠ»Ρ Π² ΠΎΠ±Π΅ΡΠΏΠ΅ΡΠ΅Π½ΠΈΠΈ ΡΡΡΠΎΠΉΡΠΈΠ²ΠΎΠ³ΠΎ ΡΠΎΡΠΈΠ°Π»ΡΠ½ΠΎ-ΡΠΊΠΎΠ½ΠΎΠΌΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΡΠ°Π·Π²ΠΈΡΠΈΡ ΡΡΡΠ°Π½Ρ ΠΈ Π°ΠΊΡΠΈΠ²Π½ΠΎΠ³ΠΎ Π½Π°ΡΠ°ΡΠΈΠ²Π°Π½ΠΈΡ ΡΠ΅Π»ΠΎΠ²Π΅ΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΊΠ°ΠΏΠΈΡΠ°Π»Π° ΠΏΡΡΠ΅ΠΌ Π½Π΅ΠΏΡΠ΅ΡΡΠ²Π½ΠΎΠ³ΠΎ Π²ΠΎΡΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄ΡΡΠ²Π° ΠΏΠ΅ΡΠ΅Π΄ΠΎΠ²ΠΎΠ³ΠΎ Π·Π½Π°Π½ΠΈΡ Π² ΡΠΎΡΠΌΠ΅ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠΉ ΠΈ ΡΠ°Π·ΡΠ°Π±ΠΎΡΠΎΠΊ Π΄Π»Ρ ΠΏΡΠΎΠΌΡΡΠ»Π΅Π½Π½ΠΎΡΡΠΈ ΠΈΠ³ΡΠ°Π΅Ρ ΠΏΠΎΠ΄Π³ΠΎΡΠΎΠ²ΠΊΠ° Π½ΠΎΠ²ΠΎΠ³ΠΎ ΠΏΠΎΠΊΠΎΠ»Π΅Π½ΠΈΡ Π°ΡΠΏΠΈΡΠ°Π½ΡΠΎΠ². ΠΠΊΡΡΠ°Π»ΡΠ½ΡΠΌΠΈ ΡΠ²Π»ΡΡΡΡΡ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°ΡΠ΅Π»ΡΡΠΊΠΈΠ΅ Π²ΠΎΠΏΡΠΎΡΡ: Π² ΡΠΈΡΠΎΠΊΠΎΠΌ ΠΊΠΎΠ½ΡΠ΅ΠΊΡΡΠ΅ - ΠΊΠ°ΠΊΠΎΠ²Π° Π΄ΠΈΠ½Π°ΠΌΠΈΠΊΠ° ΡΠ°Π·Π²ΠΈΡΠΈΡ ΡΠΎΡΡΠΈΠΉΡΠΊΠΎΠΉ Π°ΡΠΏΠΈΡΠ°Π½ΡΡΡΡ Π² ΡΠ΅Π³ΠΈΠΎΠ½Π°Ρ
ΠΈ, Π² ΡΠ°ΡΡΠ½ΠΎΡΡΠΈ, - Π½Π°ΡΠΊΠΎΠ»ΡΠΊΠΎ Π±Π»Π°Π³ΠΎΠΏΠΎΠ»ΡΡΠ½Ρ Π°ΡΠΏΠΈΡΠ°Π½ΡΡ, ΡΡΠΎΠ±Ρ ΡΡΠΏΠ΅ΡΠ½ΠΎ ΡΠ΅Π°Π»ΠΈΠ·ΠΎΠ²ΡΠ²Π°ΡΡ ΡΠ²ΠΎΠΈ ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°ΡΠ΅Π»ΡΠ½ΡΠ΅ ΡΡΠ°Π΅ΠΊΡΠΎΡΠΈΠΈ (ΡΡΡΠ΄ΠΎΡΡΡΡΠΎΠ΅Π½Ρ Π»ΠΈ, ΠΈΠΌΠ΅ΡΡ Π»ΠΈ Π² ΡΡΠΎΠΌ ΠΏΠΎΡΡΠ΅Π±Π½ΠΎΡΡΡ) ΠΈ ΠΊΠ°ΠΊΠΎΠ²Π° ΡΠΏΠ΅ΡΠΈΡΠΈΠΊΠ° ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°ΡΠ΅Π»ΡΠ½ΠΎΠΉ ΠΌΠΎΠ±ΠΈΠ»ΡΠ½ΠΎΡΡΠΈ Ρ ΡΠ΅Π»ΡΡ ΠΏΠΎΠ»ΡΡΠ΅Π½ΠΈΡ Π²ΡΡΡΠ΅ΠΉ ΠΊΠ²Π°Π»ΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ. ΠΠ°ΡΡΠ½ΡΠΉ ΠΈΠ½ΡΠ΅ΡΠ΅Ρ ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»ΡΠ΅Ρ ΠΈΠ·ΡΡΠ΅Π½ΠΈΠ΅ ΡΠ°Π·Π²ΠΈΡΠΈΡ Π°ΡΠΏΠΈΡΠ°Π½ΡΡΡΡ Ρ ΡΡΠ΅ΡΠΎΠΌ ΡΠ°ΡΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΡ ΡΠ΅ΡΡΡΡΠΎΠ² (ΡΠΈΠ½Π°Π½ΡΠΎΠ²ΡΡ
, ΠΈΠ½ΡΠ΅Π»Π»Π΅ΠΊΡΡΠ°Π»ΡΠ½ΡΡ
) ΠΏΠΎ ΡΠ΅Π³ΠΈΠΎΠ½Π°ΠΌ, Π° ΡΠ°ΠΊΠΆΠ΅ ΠΈΡ
ΠΊΠΎΠ½ΡΠ΅Π½ΡΡΠ°ΡΠΈΠΈ Π² Π²ΡΠ·Π°Ρ
, ΡΠΏΠΎΡΠΎΠ±Π½ΡΡ
Π½Π° Π³Π΅Π½Π΅ΡΠ°ΡΠΈΡ ΠΏΡΠΎΡΡΠ²Π½ΡΡ
ΠΈΠ΄Π΅ΠΉ, ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΠΉ Π² ΡΠΎΡΡΠΈΠΉΡΠΊΠΎΠΉ Π½Π°ΡΠΊΠ΅. ΠΠ½ΠΈ Π²ΡΡΡΡΠΏΠ°ΡΡ ΡΠ΅Π½ΡΡΠ°ΠΌΠΈ ΠΏΡΠΈΡΡΠΆΠ΅Π½ΠΈΡ ΠΏΡΠΎΠ°ΠΊΡΠΈΠ²Π½ΠΎΠΉ ΠΌΠΎΠ»ΠΎΠ΄Π΅ΠΆΠΈ Π² ΡΠ΅Π³ΠΈΠΎΠ½. ΠΠ»Ρ ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠΈ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½Ρ ΠΌΠ΅ΡΠΎΠ΄Ρ Π²ΡΠΎΡΠΈΡΠ½ΠΎΠ³ΠΎ Π°Π½Π°Π»ΠΈΠ·Π° Π΄Π°Π½Π½ΡΡ
, ΡΠΎΠΏΠΎΡΡΠ°Π²Π»Π΅Π½ΠΈΡ, ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ, ΠΈΠ½ΡΠΎΠ³ΡΠ°ΡΠΈΠΊΠΈ. ΠΡΠΎΠ°Π½Π°Π»ΠΈΠ·ΠΈΡΠΎΠ²Π°Π½Ρ Π±Π°Π·Ρ ΠΌΠΎΠ½ΠΈΡΠΎΡΠΈΠ½Π³Π° ΡΡΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎΡΡΠΈ Π²ΡΠ·ΠΎΠ² Π Π€ 2014-2020 Π³Π³., ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΡ ΡΠΎΡΠΈΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠΉ Π°ΡΠΏΠΈΡΠ°Π½ΡΠΎΠ² 2017-2020 Π³Π³. Π²Π΅Π΄ΡΡΠ΅Π³ΠΎ ΡΠΎΡΡΠΈΠΉΡΠΊΠΎΠ³ΠΎ Π²ΡΠ·Π°. ΠΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½Π° ΠΊΠ°ΡΡΠ° ΠΊΠΎΠ½ΡΠ΅Π½ΡΡΠ°ΡΠΈΠΈ Π°ΡΠΏΠΈΡΠ°Π½ΡΠΎΠ² Π² ΠΎΡΠ΄Π΅Π»ΡΠ½ΡΡ
ΡΠ΅Π³ΠΈΠΎΠ½Π°Ρ
Ρ ΡΡΠ΅ΡΠΎΠΌ ΡΠΈΡΠ»Π΅Π½Π½ΠΎΡΡΠΈ ΠΈ ΠΏΡΠΈΡΠΎΠΊΠ° / ΠΎΡΡΠΎΠΊΠ°. Π¦Π΅Π½ΡΡΠ°ΠΌΠΈ ΠΏΡΠΈΡΡΠΆΠ΅Π½ΠΈΡ Π°ΡΠΏΠΈΡΠ°Π½ΡΠΎΠ², ΠΏΠΎΠΌΠΈΠΌΠΎ ΡΡΠΎΠ»ΠΈΡΠ½ΡΡ
ΡΠ΅Π³ΠΈΠΎΠ½ΠΎΠ² (ΠΈΡ
Π΄ΠΎΠ»Ρ Π² ΠΎΠ±ΡΠ΅ΠΉ ΡΠΈΡΠ»Π΅Π½Π½ΠΎΡΡΠΈ - 47,9 %), ΡΠ²Π»ΡΡΡΡΡ Π Π΅ΡΠΏΡΠ±Π»ΠΈΠΊΠ° Π’Π°ΡΠ°ΡΡΡΠ°Π½ (3,2 %), Π’ΠΎΠΌΡΠΊΠ°Ρ ΠΎΠ±Π»Π°ΡΡΡ (2,4 %), Π‘Π²Π΅ΡΠ΄Π»ΠΎΠ²ΡΠΊΠ°Ρ ΠΎΠ±Π»Π°ΡΡΡ (2,1 %), Π³Π΄Π΅ ΡΠ°ΡΠΏΠΎΠ»ΠΎΠΆΠ΅Π½Ρ Π²Π΅Π΄ΡΡΠΈΠ΅ ΡΠΎΡΡΠΈΠΉΡΠΊΠΈΠ΅ Π²ΡΠ·Ρ. Π ΡΠΎΠΏ 7 ΡΠ΅Π³ΠΈΠΎΠ½ΠΎΠ² Π²Ρ
ΠΎΠ΄ΡΡ ΠΠ΅Π»Π³ΠΎΡΠΎΠ΄ΡΠΊΠ°Ρ (Π΄ΠΎΠ»Ρ Π°ΡΠΏΠΈΡΠ°Π½ΡΠΎΠ² - 2,7 %) ΠΈ Π ΠΎΡΡΠΎΠ²ΡΠΊΠ°Ρ ΠΎΠ±Π»Π°ΡΡΠΈ (2,4 %) Ρ ΡΠΈΠ»ΡΠ½ΡΠΌΠΈ Π½Π°ΡΡΠ½ΠΎ-ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°ΡΠ΅Π»ΡΡΠΊΠΈΠΌ ΠΈ ΡΠ΅Π΄Π΅ΡΠ°Π»ΡΠ½ΡΠΌ ΡΠ½ΠΈΠ²Π΅ΡΡΠΈΡΠ΅ΡΠ°ΠΌΠΈ. ΠΠ°ΠΆΠ΅ Π² ΡΠ΅Π³ΠΈΠΎΠ½Π°Ρ
, ΠΏΡΠΈΠ²Π»Π΅ΠΊΠ°ΡΡΠΈΡ
Π±ΠΎΠ»ΡΡΠΎΠ΅ ΠΊΠΎΠ»ΠΈΡΠ΅ΡΡΠ²ΠΎ ΠΎΠ±ΡΡΠ°ΡΡΠΈΡ
ΡΡ, Π½Π°Π±Π»ΡΠ΄Π°Π΅ΡΡΡ ΠΈΡ
Π½Π΅Π΄ΠΎΡΠΈΠ½Π°Π½ΡΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅ Π² ΠΏΡΠΎΡΠ΅ΡΡΠ΅ ΠΎΠ±ΡΡΠ΅Π½ΠΈΡ (85 % ΡΠΎΠ²ΠΌΠ΅ΡΠ°ΡΡ ΡΠ°Π±ΠΎΡΡ ΠΈ ΠΎΠ±ΡΡΠ΅Π½ΠΈΠ΅), ΡΠ°ΡΡΠ΅Ρ ΠΎΡΡΠΎΠΊ Π² Π·Π°ΡΡΠ±Π΅ΠΆΠ½ΡΠ΅ Π²ΡΠ·Ρ. Π Π΅Π·ΡΠ»ΡΡΠ°ΡΡ ΠΌΠΎΠ³ΡΡ Π±ΡΡΡ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½Ρ Π΄Π»Ρ ΡΠΎΠ²Π΅ΡΡΠ΅Π½ΡΡΠ²ΠΎΠ²Π°Π½ΠΈΡ ΠΏΡΠΈΠ½ΡΠΈΠΏΠΎΠ² ΡΠ°Π·Π²ΠΈΡΠΈΡ Π°ΡΠΏΠΈΡΠ°Π½ΡΡΡΡ ΠΈ ΠΌΠ΅Ρ
Π°Π½ΠΈΠ·ΠΌΠΎΠ² ΠΏΠΎΠ΄Π΄Π΅ΡΠΆΠΊΠΈ Π°ΡΠΏΠΈΡΠ°Π½ΡΠΎΠ² Π² Π²Π΅Π΄ΡΡΠΈΡ
ΠΈ ΠΈΠ½ΡΡ
ΡΠ΅Π³ΠΈΠΎΠ½Π°Π»ΡΠ½ΡΡ
Π²ΡΠ·Π°Ρ
Ρ ΡΠ΅Π»ΡΡ ΡΡΡΠΎΠΉΡΠΈΠ²ΠΎΠ³ΠΎ ΡΠ°Π·Π²ΠΈΡΠΈΡ ΡΠ΅ΡΡΠΈΡΠΎΡΠΈΠΉ.Training of a new generation of graduate students plays a key role in ensuring a countryβs 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 studentsβ 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.ΠΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠ΅ Π²ΡΠΏΠΎΠ»Π½Π΅Π½ΠΎ ΠΏΡΠΈ ΡΠΈΠ½Π°Π½ΡΠΎΠ²ΠΎΠΉ ΠΏΠΎΠ΄Π΄Π΅ΡΠΆΠΊΠ΅ ΠΠΈΠ½ΠΈΡΡΠ΅ΡΡΡΠ²Π° Π½Π°ΡΠΊΠΈ ΠΈ Π²ΡΡΡΠ΅Π³ΠΎ ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΡ Π ΠΎΡΡΠΈΠΉΡΠΊΠΎΠΉ Π€Π΅Π΄Π΅ΡΠ°ΡΠΈΠΈ Π² ΡΠ°ΠΌΠΊΠ°Ρ
ΠΡΠΎΠ³ΡΠ°ΠΌΠΌΡ ΡΠ°Π·Π²ΠΈΡΠΈΡ Π£ΡΠ°Π»ΡΡΠΊΠΎΠ³ΠΎ ΡΠ΅Π΄Π΅ΡΠ°Π»ΡΠ½ΠΎΠ³ΠΎ ΡΠ½ΠΈΠ²Π΅ΡΡΠΈΡΠ΅ΡΠ° ΠΈΠΌΠ΅Π½ΠΈ ΠΏΠ΅ΡΠ²ΠΎΠ³ΠΎ ΠΡΠ΅Π·ΠΈΠ΄Π΅Π½ΡΠ° Π ΠΎΡΡΠΈΠΈ Π. Π. ΠΠ»ΡΡΠΈΠ½Π° Π² ΡΠΎΠΎΡΠ²Π΅ΡΡΡΠ²ΠΈΠΈ Ρ ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΠΎΠΉ ΡΡΡΠ°ΡΠ΅Π³ΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ Π°ΠΊΠ°Π΄Π΅ΠΌΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ Π»ΠΈΠ΄Π΅ΡΡΡΠ²Π° Β«ΠΡΠΈΠΎΡΠΈΡΠ΅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
ΠΠ°Π½Π΄Π΅ΠΌΠΈΡ ΡΡΠ°Π»Π° ΠΊΠ°ΡΠ°Π»ΠΈΠ·Π°ΡΠΎΡΠΎΠΌ Π½Π΅ΠΈΠ·Π±Π΅ΠΆΠ½ΠΎΠ³ΠΎ ΠΏΡΠΎΡΠ΅ΡΡΠ° ΡΠΈΡΡΠΎΠ²ΠΈΠ·Π°ΡΠΈΠΈ ΠΊΠΎΠΌΠΌΡΠ½ΠΈΠΊΠ°ΡΠΈΠΉ, ΡΡΡΠ΅ΠΌΠΈΡΠ΅Π»ΡΠ½ΠΎ ΠΈΠ·ΠΌΠ΅Π½ΠΈΠ² ΠΎΡΠ³Π°Π½ΠΈΠ·Π°ΡΠΈΡ ΠΈ ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΠΈ ΡΠ΅Π°Π»ΠΈΠ·Π°ΡΠΈΠΈ ΠΏΡΠΎΡΠ΅ΡΡΠΈΠΎΠ½Π°Π»ΡΠ½ΠΎΠΉ Π΄Π΅ΡΡΠ΅Π»ΡΠ½ΠΎΡΡΠΈ ΠΌΠΈΠ»Π»ΠΈΠΎΠ½ΠΎΠ² ΡΠΎΡΡΡΠ΄Π½ΠΈΠΊΠΎΠ² ΠΏΠΎ Π²ΡΠ΅ΠΌΡ ΠΌΠΈΡΡ. Π ΠΎΡΡ Π±Π΅Π·ΡΠ°Π±ΠΎΡΠΈΡΡ, ΠΏΠ΅ΡΠ΅Π²ΠΎΠ΄ Π²ΡΠ΅Ρ
Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΡΡ
ΠΏΡΠΎΡΠ΅ΡΡΠΈΠΎΠ½Π°Π»ΡΠ½ΡΡ
Π³ΡΡΠΏΠΏ Π½Π° Π΄ΠΈΡΡΠ°Π½ΡΠΈΠΎΠ½Π½ΡΡ ΡΠ°Π±ΠΎΡΡ, ΠΎΠ±ΡΡΠ»ΠΎΠ²Π»Π΅Π½Π½ΡΠ΅ Π²Π½Π΅ΡΠ½Π΅ΠΉ Π½Π΅ΠΎΠ±Ρ
ΠΎΠ΄ΠΈΠΌΠΎΡΡΡΡ Π² ΠΈΠ·ΠΎΠ»ΡΡΠΈΠΈ Π΄Π»Ρ ΠΌΠΈΠ½ΠΈΠΌΠΈΠ·Π°ΡΠΈΠΈ ΡΠ°ΡΠΏΡΠΎΡΡΡΠ°Π½Π΅Π½ΠΈΡ Π² 2020 Π³ΠΎΠ΄Ρ Covid-19, Π²Π΅Π΄ΡΡ ΠΊ ΡΠ°Π΄ΠΈΠΊΠ°Π»ΡΠ½ΠΎΠΌΡ ΠΈΠ·ΠΌΠ΅Π½Π΅Π½ΠΈΡ ΡΡΠ½ΠΊΠ° ΡΡΡΠ΄Π°. ΠΠΎΠΈΡΠΊ ΠΎΡΠ²Π΅ΡΠΎΠ² Π½Π° Π½ΠΎΠ²ΡΠ΅ Π²ΡΠ·ΠΎΠ²Ρ Π΄Π΅ΡΠ΅Π³ΡΠ»ΡΡΠΈΠΈ ΡΡΡΠ΄ΠΎΠ²ΡΡ
ΠΎΡΠ½ΠΎΡΠ΅Π½ΠΈΠΉ Π²ΠΎΠ·ΠΌΠΎΠΆΠ΅Π½ ΠΏΡΡΠ΅ΠΌ ΠΈΠ·ΡΡΠ΅Π½ΠΈΡ ΠΏΡΠΎΡΠ΅ΡΡΠΎΠ² ΠΏΡΠ΅ΠΊΠ°ΡΠΈΠ·Π°ΡΠΈΠΈ Π½Π° ΡΡΠ½ΠΊΠ΅ ΡΡΡΠ΄Π°. ΠΠ°ΡΡΠ½ΡΠΉ ΠΈΠ½ΡΠ΅ΡΠ΅Ρ ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»ΡΠ΅Ρ ΠΈΠ·ΡΡΠ΅Π½ΠΈΠ΅ ΡΡΠ°ΡΡΠΈΡ Π² ΡΡΠΈΡ
ΠΏΡΠΎΡΠ΅ΡΡΠ°Ρ
ΠΌΠΎΠ»ΠΎΠ΄Π΅ΠΆΠΈ, ΠΊΠΎΡΠΎΡΠ°Ρ Π²ΠΎΡΠΏΡΠΈΠΈΠΌΡΠΈΠ²Π° ΠΊ ΡΠΎΡΠΈΠ°Π»ΡΠ½ΡΠΌ ΠΈΠ½Π½ΠΎΠ²Π°ΡΠΈΡΠΌ ΠΈ ΠΎΠ±Π»Π°Π΄Π°Π΅Ρ Π²ΡΡΠΎΠΊΠΈΠΌΠΈ ΠΊΠΎΠΌΠΏΠ΅ΡΠ΅Π½ΡΠΈΡΠΌΠΈ Π² ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΎΠ½Π½ΡΡ
ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΡΡ
. ΠΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠ΅ ΠΏΡΠΎΡΠ΅ΡΡΠΈΠΎΠ½Π°Π»ΡΠ½ΡΡ
ΡΡΠ°Π΅ΠΊΡΠΎΡΠΈΠΉ Π²ΡΠΏΡΡΠΊΠ½ΠΈΠΊΠΎΠ² Π²ΡΠ·ΠΎΠ², ΡΡΡΠ΄ΠΎΡΡΡΡΠΎΠ΅Π½Π½ΡΡ
Π² Π£ΡΠ°Π»ΡΡΠΊΠΎΠΌ ΡΠ΅Π³ΠΈΠΎΠ½Π΅ ΠΈ Π·Π° Π΅Π³ΠΎ ΠΏΡΠ΅Π΄Π΅Π»Π°ΠΌΠΈ, ΠΏΠΎΠ·Π²ΠΎΠ»ΡΠ΅Ρ Π²ΡΡΠ²ΠΈΡΡ, ΠΌΠΎΠ³ΡΡ Π»ΠΈ Π±ΡΡΡ ΡΡΠΏΠ΅ΡΠ½Ρ ΡΠΎΡΠΌΡ ΠΏΡΠ΅ΠΊΠ°ΡΠ½ΠΎΠΉ Π·Π°Π½ΡΡΠΎΡΡΠΈ Π½Π° ΡΡΠ½ΠΊΠ΅ ΡΡΡΠ΄Π°, ΠΊΠ°ΠΊΠΎΠ²Ρ Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΡΡΠΈΠΊΠΈ Π²ΠΎΠ²Π»Π΅ΡΠ΅Π½Π½ΡΡ
Π² ΡΡΠΈ ΡΠΎΡΠΌΡ ΡΠΎΡΠΈΠ°Π»ΡΠ½ΡΡ
Π³ΡΡΠΏΠΏ. ΠΠ²ΡΠΎΡΡ ΠΈΡΠΏΠΎΠ»ΡΠ·ΡΡΡ ΠΌΠΎΠ½ΠΈΡΠΎΡΠΈΠ½Π³ Π²ΡΠΏΡΡΠΊΠ½ΠΈΠΊΠΎΠ² Π²ΡΠ·Π° 2017-2019 Π³Π³., ΠΏΡΠΎΠ²Π΅Π΄Π΅Π½Π½ΡΠΉ Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ ΠΎΠΏΡΠΎΡΠ½ΡΡ
ΠΈ Π°Π΄ΠΌΠΈΠ½ΠΈΡΡΡΠ°ΡΠΈΠ²Π½ΡΡ
Π΄Π°Π½Π½ΡΡ
. ΠΠ»Ρ ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠΈ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½Ρ ΠΌΠ΅ΡΠΎΠ΄Ρ ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ, ΡΠΊΡΠΏΠ΅ΡΡΠ½ΡΡ
ΠΎΡΠ΅Π½ΠΎΠΊ. ΠΠ½Π°Π»ΠΈΠ· ΠΏΠΎΠΊΠ°Π·Π°Π», ΡΡΠΎ 34,4 % Π²ΡΠΏΡΡΠΊΠ½ΠΈΠΊΠΎΠ² Π²ΡΠ·Π° ΠΎΡΠ½ΠΎΡΡΡΡΡ ΠΊ ΠΏΡΠ΅ΠΊΠ°ΡΠΈΠ°ΡΡ, ΠΈΠ· Π½ΠΈΡ
Π»ΠΈΡΡ 8,8 % - Π±Π΅Π·ΡΠ°Π±ΠΎΡΠ½ΡΠ΅. ΠΠΎΠ»ΠΎΠ΄ΡΠ΅ ΡΠΏΠ΅ΡΠΈΠ°Π»ΠΈΡΡΡ, Π·Π°Π½ΡΡΡΠ΅ Π² ΡΠΎΡΠΌΠ΅ ΡΡΠΈΠ»Π°Π½ΡΠ° ΠΈ Π² ΡΡΠ΅ΡΠ΅ IT-ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΠΉ, ΡΡΠΏΠ΅ΡΠ½ΠΎ ΡΡΡΠ΄ΠΎΡΡΡΡΠΎΠ΅Π½Ρ, ΠΈΠΌΠ΅ΡΡ Π²ΡΡΠΎΠΊΠΈΠ΅ ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»ΠΈ ΡΡΡΠ΄ΠΎΡΡΡΡΠΎΠΉΡΡΠ²Π° ΠΏΠΎ ΡΠΏΠ΅ΡΠΈΠ°Π»ΡΠ½ΠΎΡΡΠΈ, Π·Π°ΡΠ°Π±ΠΎΡΠ½ΠΎΠΉ ΠΏΠ»Π°ΡΠ΅, ΡΠ΄ΠΎΠ²Π»Π΅ΡΠ²ΠΎΡΠ΅Π½Π½ΠΎΡΡΠΈ ΡΠ°Π±ΠΎΡΠΎΠΉ. Π Π΅Π·ΡΠ»ΡΡΠ°ΡΡ ΠΏΡΠΈΠΌΠ΅Π½ΠΈΠΌΡ Π΄Π»Ρ Π±Π°Π»Π°Π½ΡΠΈΡΠΎΠ²ΠΊΠΈ Π½Π΅ΡΡΡΠΎΠΉΡΠΈΠ²ΠΎΠΉ Π·Π°Π½ΡΡΠΎΡΡΠΈ ΠΈ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΡ Π΅Π΅ Π»ΡΡΡΠΈΡ
ΠΏΡΠ°ΠΊΡΠΈΠΊ, Π½Π°ΠΊΠΎΠΏΠ»Π΅Π½Π½ΡΡ
ΡΡΠΈΠ»Π°Π½ΡΠ΅ΡΠ°ΠΌΠΈ ΠΈ ΡΠΏΠ΅ΡΠΈΠ°Π»ΠΈΡΡΠ°ΠΌΠΈ Π² ΡΡΠ΅ΡΠ΅ IT-ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΠΉ, ΠΊΠ°ΠΊ ΡΠΎΡΠΈΠ°Π»ΡΠ½ΠΎΠ³ΠΎ ΠΈΠ½ΡΡΡΡΠΌΠ΅Π½ΡΠ° ΡΠ΅Π³ΡΠ»ΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΡΡΡΠ΄ΠΎΠ²ΡΡ
ΠΎΡΠ½ΠΎΡΠ΅Π½ΠΈΠΉ Π² ΡΠ»ΠΎΠΆΠ½ΠΎΠΉ ΡΠΏΠΈΠ΄Π΅ΠΌΠΈΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΎΠΉ ΡΠΈΡΡΠ°ΡΠΈΠΈ.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.ΠΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠ΅ ΠΏΠΎΠ΄Π΄Π΅ΡΠΆΠ°Π½ΠΎ ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΠΎΠΉ 211 ΠΡΠ°Π²ΠΈΡΠ΅Π»ΡΡΡΠ²Π° Π ΠΎΡΡΠΈΠΉΡΠΊΠΎΠΉ Π€Π΅Π΄Π΅ΡΠ°ΡΠΈΠΈ, ΠΊΠΎΠ½ΡΡΠ°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 COVIDβ19 Pandemic
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.ΠΠ°Π½Π΄Π΅ΠΌΠΈΡ COVID-19 ΠΎΠΊΠ°Π·Π°Π»Π° ΡΡΡΠ΅ΡΡΠ²Π΅Π½Π½ΠΎΠ΅ Π²Π»ΠΈΡΠ½ΠΈΠ΅ Π½Π° ΡΠΊΠΎΠ½ΠΎΠΌΠΈΠΊΡ Π½Π° Π²ΡΠ΅Ρ
ΡΡΠΎΠ²Π½ΡΡ
, ΠΎΡ Π³Π»ΠΎΠ±Π°Π»ΡΠ½ΡΡ
ΡΡΠ½ΠΊΠΎΠ² Π΄ΠΎ ΠΌΠΈΠΊΡΠΎΠΏΡΠ΅Π΄ΠΏΡΠΈΡΡΠΈΠΉ. ΠΡΠΈ ΡΡΠΎΠΌ ΠΏΠ°Π½Π΄Π΅ΠΌΠΈΡ ΠΈ Π΅Π΅ ΠΏΠΎΡΠ»Π΅Π΄ΡΡΠ²ΠΈΡ ΠΎΡΡΠ°Π²ΠΈΠ»ΠΈ ΡΠΈΡΠΎΠΊΠΈΠΉ ΡΠΈΡΡΠΎΠ²ΠΎΠΉ ΡΠ»Π΅Π΄. ΠΠ³ΠΎ ΠΈΠ·ΡΡΠ΅Π½ΠΈΠ΅ ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»ΡΠ΅ΡΡΡ ΡΡΠ΅Π·Π²ΡΡΠ°ΠΉΠ½ΠΎ Π°ΠΊΡΡΠ°Π»ΡΠ½ΡΠΌ, ΡΠ°ΠΊ ΠΊΠ°ΠΊ ΠΏΠΎΠ΄Ρ
ΠΎΠ΄Ρ ΠΊ Π°Π½Π°Π»ΠΈΠ·Ρ ΡΠΈΡΡΠΎΠ²ΠΎΠ³ΠΎ ΡΠ»Π΅Π΄Π° ΠΏΠ°Π½Π΄Π΅ΠΌΠΈΠΈ ΠΈ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ Π΅Π³ΠΎ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠΎΠ² Π΄Π»Ρ ΡΠΏΡΠ°Π²Π»Π΅Π½ΠΈΡ ΡΠΈΡΠΊΠ°ΠΌΠΈ ΠΌΠΎΠ³ΡΡ Π±ΡΡΡ ΡΡΠΏΠ΅ΡΠ½ΠΎ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½Ρ Π² ΡΠ»ΡΡΠ°Π΅ Π²ΠΎΠ·Π½ΠΈΠΊΠ½ΠΎΠ²Π΅Π½ΠΈΡ Π°Π½Π°Π»ΠΎΠ³ΠΈΡΠ½ΡΡ
ΡΠ³ΡΠΎΠ·. ΠΠΊΡΡΠ°Π»ΡΠ½ΠΎΡΡΡ ΠΏΡΠΎΠ±Π»Π΅ΠΌΡ ΠΏΡΠΈΠ·Π½Π°Π΅ΡΡΡ ΠΈ ΡΡΠ΅Π½ΡΠΌΠΈ-ΡΠΊΠΎΠ½ΠΎΠΌΠΈΡΡΠ°ΠΌΠΈ, ΠΊΠΎΡΠΎΡΡΠ΅ ΠΎΡΠΌΠ΅ΡΠ°ΡΡ ΡΡΡΠ΅ΡΡΠ²Π΅Π½Π½ΠΎΠ΅ Π²Π»ΠΈΡΠ½ΠΈΠ΅ ΠΏΠ°Π½Π΄Π΅ΠΌΠΈΠΈ Π½Π° ΡΠΊΠΎΠ½ΠΎΠΌΠΈΠΊΡ ΠΈ ΡΠΊΠΎΠ½ΠΎΠΌΠΈΡΠ΅ΡΠΊΡΡ ΡΠ΅ΠΎΡΠΈΡ Π² ΡΠ΅Π»ΠΎΠΌ. Π¦Π΅Π»ΡΡ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ ΡΠ²Π»ΡΠ΅ΡΡΡ ΡΠ°Π·ΡΠ°Π±ΠΎΡΠΊΠ° ΠΏΠΎΠ΄Ρ
ΠΎΠ΄ΠΎΠ² ΠΊ ΠΎΠΏΠ΅ΡΠ°ΡΠΈΠ²Π½ΠΎΠΉ ΠΊΠΎΠ»ΠΈΡΠ΅ΡΡΠ²Π΅Π½Π½ΠΎΠΉ ΠΎΡΠ΅Π½ΠΊΠ΅ Π²Π»ΠΈΡΠ½ΠΈΡ ΠΏΠ°Π½Π΄Π΅ΠΌΠΈΠΈ Π½Π° ΡΠ½ΠΈΠ²Π΅ΡΡΠΈΡΠ΅Ρ Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ Π΄Π°Π½Π½ΡΡ
ΡΡΠ΅ΡΠ½ΡΡ
ΡΠΈΠ½Π°Π½ΡΠΎΠ²ΡΡ
ΡΠΈΡΡΠ΅ΠΌ, ΠΈΡ
Π°ΠΏΡΠΎΠ±Π°ΡΠΈΡ ΠΈ Π³Π΅Π½Π΅ΡΠ°ΡΠΈΡ ΠΏΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½ΠΈΠΉ ΠΏΠΎ ΠΌΠΈΠ½ΠΈΠΌΠΈΠ·Π°ΡΠΈΠΈ ΡΠΈΡΠΊΠΎΠ² ΡΠΈΠ½Π°Π½ΡΠΎΠ²ΠΎ-Ρ
ΠΎΠ·ΡΠΉΡΡΠ²Π΅Π½Π½ΠΎΠΉ Π΄Π΅ΡΡΠ΅Π»ΡΠ½ΠΎΡΡΠΈ. ΠΠ°ΡΡΠ½Π°Ρ Π³ΠΈΠΏΠΎΡΠ΅Π·Π° ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ ΡΠΎΡΡΠΎΠΈΡ Π² ΡΠΎΠΌ, ΡΡΠΎ Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ Π°Π½Π°Π»ΠΈΠ·Π° Π΄Π°Π½Π½ΡΡ
, ΠΏΠ΅ΡΠ΅Π΄Π°Π²Π°Π΅ΠΌΡΡ
ΡΠΎΠ½Π΄Ρ ΡΠΎΡΠΈΠ°Π»ΡΠ½ΠΎΠ³ΠΎ ΡΡΡΠ°Ρ
ΠΎΠ²Π°Π½ΠΈΡ, ΠΎ Π½Π΅ΡΡΡΠ΄ΠΎΡΠΏΠΎΡΠΎΠ±Π½ΠΎΡΡΠΈ ΡΠΎΡΡΡΠ΄Π½ΠΈΠΊΠΎΠ² ΠΌΠΎΠΆΠ΅Ρ Π±ΡΡΡ ΠΏΠΎΠ²ΡΡΠ΅Π½Π° ΡΡΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎΡΡΡ ΡΠΏΡΠ°Π²Π»Π΅Π½ΠΈΡ ΡΠΈΡΠΊΠ°ΠΌΠΈ ΡΠΈΠ½Π°Π½ΡΠΎΠ²ΠΎ-Ρ
ΠΎΠ·ΡΠΉΡΡΠ²Π΅Π½Π½ΠΎΠΉ Π΄Π΅ΡΡΠ΅Π»ΡΠ½ΠΎΡΡΠΈ Π² ΡΡΠ»ΠΎΠ²ΠΈΡΡ
ΠΏΠ°Π½Π΄Π΅ΠΌΠΈΠΈ Π½Π° ΡΡΠΎΠ²Π½Π΅ ΡΠ½ΠΈΠ²Π΅ΡΡΠΈΡΠ΅ΡΠ°. Π ΠΎΡΡ ΡΡΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎΡΡΠΈ ΠΎΠ±Π΅ΡΠΏΠ΅ΡΠΈΠ²Π°Π΅ΡΡΡ ΠΊΠΎΡΡΠ΅ΠΊΡΠΈΠ΅ΠΉ ΠΏΠ»Π°Π½ΠΎΠ² ΠΏΠΎ ΠΌΠΈΠ½ΠΈΠΌΠΈΠ·Π°ΡΠΈΠΈ ΡΠΈΡΠΊΠΎΠ² Ρ ΡΡΠ΅ΡΠΎΠΌ Π½Π΅ΠΎΠ΄Π½ΠΎΡΠΎΠ΄Π½ΠΎΡΡΠΈ Π²Π»ΠΈΡΠ½ΠΈΡ ΠΏΠ°Π½Π΄Π΅ΠΌΠΈΠΈ Π½Π° ΡΠΎΡΡΡΠ΄Π½ΠΈΠΊΠΎΠ² Π² Π·Π°Π²ΠΈΡΠΈΠΌΠΎΡΡΠΈ ΠΎΡ Π²ΠΎΠ·ΡΠ°ΡΡΠ°, Π³Π΅Π½Π΄Π΅ΡΠ°, ΠΏΡΠΈΠ½Π°Π΄Π»Π΅ΠΆΠ½ΠΎΡΡΠΈ ΠΊ ΠΊΠ°ΡΠ΅Π³ΠΎΡΠΈΠΈ ΠΏΠ΅ΡΡΠΎΠ½Π°Π»Π°. ΠΠ»Ρ ΠΈΠ½ΡΠ΅Π³ΡΠ°ΡΠΈΠΈ ΠΈ Π°Π½Π°Π»ΠΈΠ·Π° Π΄Π°Π½Π½ΡΡ
Π°Π²ΡΠΎΡΠ°ΠΌΠΈ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π»ΠΈΡΡ ΠΏΠΎΠ΄Ρ
ΠΎΠ΄Ρ Data Science. ΠΠ° ΠΏΡΠΈΠΌΠ΅ΡΠ΅ Π΄Π°Π½Π½ΡΡ
Π£ΡΠ°Π»ΡΡΠΊΠΎΠ³ΠΎ ΡΠ΅Π΄Π΅ΡΠ°Π»ΡΠ½ΠΎΠ³ΠΎ ΡΠ½ΠΈΠ²Π΅ΡΡΠΈΡΠ΅ΡΠ° ΠΏΠΎΠΊΠ°Π·Π°Π½Π° ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠ²Π½ΠΎΡΡΡ Π°Π½Π°Π»ΠΈΠ·ΠΈΡΡΠ΅ΠΌΡΡ
Π΄Π°Π½Π½ΡΡ
ΠΈ ΠΊΠ°ΠΊΠΈΠ΅ ΡΠΏΡΠ°Π²Π»Π΅Π½ΡΠ΅ΡΠΊΠΈΠ΅ ΡΠ΅ΡΠ΅Π½ΠΈΡ ΠΏΠΎ ΠΌΠΈΠ½ΠΈΠΌΠΈΠ·Π°ΡΠΈΠΈ ΡΠΈΡΠΊΠΎΠ² ΠΌΠΎΠ³ΡΡ Π±ΡΡΡ ΠΏΡΠΈΠ½ΡΡΡ Π½Π° ΠΈΡ
ΠΎΡΠ½ΠΎΠ²Π΅. ΠΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½ ΠΏΠΎΠ΄Ρ
ΠΎΠ΄ ΠΊ ΠΊΠΎΠ»ΠΈΡΠ΅ΡΡΠ²Π΅Π½Π½ΠΎΠΌΡ Π°Π½Π°Π»ΠΈΠ·Ρ Π²ΠΎΠ·Π΄Π΅ΠΉΡΡΠ²ΠΈΡ ΠΏΠ°Π½Π΄Π΅ΠΌΠΈΠΈ Π½Π° ΡΠΎΡΡΡΠ΄Π½ΠΈΠΊΠΎΠ² ΡΡΠΈΠ΄ΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ Π»ΠΈΡΠ°. ΠΡΠΎΠ΄Π΅ΠΌΠΎΠ½ΡΡΡΠΈΡΠΎΠ²Π°Π½Π° ΡΡΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎΡΡΡ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΡ Π΄ΠΈΡΡΠ°Π½ΡΠΈΠΎΠ½Π½ΠΎΠ³ΠΎ ΠΎΠ±ΡΡΠ΅Π½ΠΈΡ Π΄Π»Ρ ΠΏΡΠΎΡΠΈΠ²ΠΎΠ΄Π΅ΠΉΡΡΠ²ΠΈΡ ΠΏΠ°Π½Π΄Π΅ΠΌΠΈΠΈ, ΡΡΠ·Π²ΠΈΠΌΠΎΡΡΡ Π΄Π»Ρ ΠΏΠ°Π½Π΄Π΅ΠΌΠΈΠΈ ΠΎΡΠ΄Π΅Π»ΡΠ½ΡΡ
ΠΊΠ°ΡΠ΅Π³ΠΎΡΠΈΠΉ ΡΠΎΡΡΡΠ΄Π½ΠΈΠΊΠΎΠ², Π³Π΅Π½Π΄Π΅ΡΠ½Π°Ρ ΡΡΡΡΠΊΡΡΡΠ° Π½Π΅ΡΡΡΠ΄ΠΎΡΠΏΠΎΡΠΎΠ±Π½ΠΎΡΡΠΈ. Π’Π΅ΠΎΡΠ΅ΡΠΈΡΠ΅ΡΠΊΠ°Ρ Π·Π½Π°ΡΠΈΠΌΠΎΡΡΡ ΡΠ°Π±ΠΎΡΡ ΡΠΎΡΡΠΎΠΈΡ Π² ΡΠ°Π·ΡΠ°Π±ΠΎΡΠΊΠ΅ ΠΏΠΎΠ΄Ρ
ΠΎΠ΄ΠΎΠ² ΠΊ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΡ ΡΠΈΠ½Π°Π½ΡΠΎΠ²ΠΎΠΉ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈ Π΄Π»Ρ ΡΠΎΠ²Π΅ΡΡΠ΅Π½ΡΡΠ²ΠΎΠ²Π°Π½ΠΈΡ ΡΠΏΡΠ°Π²Π»Π΅Π½ΠΈΡ ΡΠΈΡΠΊΠ°ΠΌΠΈ. ΠΠΎΠ»ΡΡΠ΅Π½Π½Π°Ρ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΡ ΠΌΠΎΠΆΠ΅Ρ Π±ΡΡΡ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½Π° Π½Π° ΠΏΡΠ°ΠΊΡΠΈΠΊΠ΅, Π² ΡΠ°ΡΡΠ½ΠΎΡΡΠΈ, Π΄Π»Ρ ΡΡΠΎΡΠ½Π΅Π½ΠΈΡ ΡΠ°ΡΡΠ΅ΡΠ° ΡΠ΅Π·Π΅ΡΠ²ΠΎΠ², ΡΠΎΠ²Π΅ΡΡΠ΅Π½ΡΡΠ²ΠΎΠ²Π°Π½ΠΈΠ΅ ΡΠ΅Ρ
Π½ΠΈΡΠ΅ΡΠΊΠΈΡ
Π·Π°Π΄Π°Π½ΠΈΠΉ ΠΏΡΠΈ ΡΠ°Π·ΡΠ°Π±ΠΎΡΠΊΠ΅ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΎΠ½Π½ΡΡ
ΡΠΈΡΡΠ΅ΠΌ.ΠΠ°Π½Π΄Π΅ΠΌΠΈΡ COVID-19 ΠΎΠΊΠ°Π·Π°Π»Π° ΡΡΡΠ΅ΡΡΠ²Π΅Π½Π½ΠΎΠ΅ Π²Π»ΠΈΡΠ½ΠΈΠ΅ Π½Π° ΡΠΊΠΎΠ½ΠΎΠΌΠΈΠΊΡ Π½Π° Π²ΡΠ΅Ρ
ΡΡΠΎΠ²Π½ΡΡ
, ΠΎΡ Π³Π»ΠΎΠ±Π°Π»ΡΠ½ΡΡ
ΡΡΠ½ΠΊΠΎΠ² Π΄ΠΎ ΠΌΠΈΠΊΡΠΎΠΏΡΠ΅Π΄ΠΏΡΠΈΡΡΠΈΠΉ. ΠΡΠΈ ΡΡΠΎΠΌ ΠΏΠ°Π½Π΄Π΅ΠΌΠΈΡ ΠΈ Π΅Π΅ ΠΏΠΎΡΠ»Π΅Π΄ΡΡΠ²ΠΈΡ ΠΎΡΡΠ°Π²ΠΈΠ»ΠΈ ΡΠΈΡΠΎΠΊΠΈΠΉ ΡΠΈΡΡΠΎΠ²ΠΎΠΉ ΡΠ»Π΅Π΄. ΠΠ³ΠΎ ΠΈΠ·ΡΡΠ΅Π½ΠΈΠ΅ ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»ΡΠ΅ΡΡΡ ΡΡΠ΅Π·Π²ΡΡΠ°ΠΉΠ½ΠΎ Π°ΠΊΡΡΠ°Π»ΡΠ½ΡΠΌ, ΡΠ°ΠΊ ΠΊΠ°ΠΊ ΠΏΠΎΠ΄Ρ
ΠΎΠ΄Ρ ΠΊ Π°Π½Π°Π»ΠΈΠ·Ρ ΡΠΈΡΡΠΎΠ²ΠΎΠ³ΠΎ ΡΠ»Π΅Π΄Π° ΠΏΠ°Π½Π΄Π΅ΠΌΠΈΠΈ ΠΈ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ Π΅Π³ΠΎ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠΎΠ² Π΄Π»Ρ ΡΠΏΡΠ°Π²Π»Π΅Π½ΠΈΡ ΡΠΈΡΠΊΠ°ΠΌΠΈ ΠΌΠΎΠ³ΡΡ Π±ΡΡΡ ΡΡΠΏΠ΅ΡΠ½ΠΎ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½Ρ Π² ΡΠ»ΡΡΠ°Π΅ Π²ΠΎΠ·Π½ΠΈΠΊΠ½ΠΎΠ²Π΅Π½ΠΈΡ Π°Π½Π°Π»ΠΎΠ³ΠΈΡΠ½ΡΡ
ΡΠ³ΡΠΎΠ·. ΠΠΊΡΡΠ°Π»ΡΠ½ΠΎΡΡΡ ΠΏΡΠΎΠ±Π»Π΅ΠΌΡ ΠΏΡΠΈΠ·Π½Π°Π΅ΡΡΡ ΠΈ ΡΡΠ΅Π½ΡΠΌΠΈ-ΡΠΊΠΎΠ½ΠΎΠΌΠΈΡΡΠ°ΠΌΠΈ, ΠΊΠΎΡΠΎΡΡΠ΅ ΠΎΡΠΌΠ΅ΡΠ°ΡΡ ΡΡΡΠ΅ΡΡΠ²Π΅Π½Π½ΠΎΠ΅ Π²Π»ΠΈΡΠ½ΠΈΠ΅ ΠΏΠ°Π½Π΄Π΅ΠΌΠΈΠΈ Π½Π° ΡΠΊΠΎΠ½ΠΎΠΌΠΈΠΊΡ ΠΈ ΡΠΊΠΎΠ½ΠΎΠΌΠΈΡΠ΅ΡΠΊΡΡ ΡΠ΅ΠΎΡΠΈΡ Π² ΡΠ΅Π»ΠΎΠΌ. Π¦Π΅Π»ΡΡ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ ΡΠ²Π»ΡΠ΅ΡΡΡ ΡΠ°Π·ΡΠ°Π±ΠΎΡΠΊΠ° ΠΏΠΎΠ΄Ρ
ΠΎΠ΄ΠΎΠ² ΠΊ ΠΎΠΏΠ΅ΡΠ°ΡΠΈΠ²Π½ΠΎΠΉ ΠΊΠΎΠ»ΠΈΡΠ΅ΡΡΠ²Π΅Π½Π½ΠΎΠΉ ΠΎΡΠ΅Π½ΠΊΠ΅ Π²Π»ΠΈΡΠ½ΠΈΡ ΠΏΠ°Π½Π΄Π΅ΠΌΠΈΠΈ Π½Π° ΡΠ½ΠΈΠ²Π΅ΡΡΠΈΡΠ΅Ρ Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ Π΄Π°Π½Π½ΡΡ
ΡΡΠ΅ΡΠ½ΡΡ
ΡΠΈΠ½Π°Π½ΡΠΎΠ²ΡΡ
ΡΠΈΡΡΠ΅ΠΌ, ΠΈΡ
Π°ΠΏΡΠΎΠ±Π°ΡΠΈΡ ΠΈ Π³Π΅Π½Π΅ΡΠ°ΡΠΈΡ ΠΏΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½ΠΈΠΉ ΠΏΠΎ ΠΌΠΈΠ½ΠΈΠΌΠΈΠ·Π°ΡΠΈΠΈ ΡΠΈΡΠΊΠΎΠ² ΡΠΈΠ½Π°Π½ΡΠΎΠ²ΠΎ-Ρ
ΠΎΠ·ΡΠΉΡΡΠ²Π΅Π½Π½ΠΎΠΉ Π΄Π΅ΡΡΠ΅Π»ΡΠ½ΠΎΡΡΠΈ. ΠΠ°ΡΡΠ½Π°Ρ Π³ΠΈΠΏΠΎΡΠ΅Π·Π° ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ ΡΠΎΡΡΠΎΠΈΡ Π² ΡΠΎΠΌ, ΡΡΠΎ Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ Π°Π½Π°Π»ΠΈΠ·Π° Π΄Π°Π½Π½ΡΡ
, ΠΏΠ΅ΡΠ΅Π΄Π°Π²Π°Π΅ΠΌΡΡ
ΡΠΎΠ½Π΄Ρ ΡΠΎΡΠΈΠ°Π»ΡΠ½ΠΎΠ³ΠΎ ΡΡΡΠ°Ρ
ΠΎΠ²Π°Π½ΠΈΡ, ΠΎ Π½Π΅ΡΡΡΠ΄ΠΎΡΠΏΠΎΡΠΎΠ±Π½ΠΎΡΡΠΈ ΡΠΎΡΡΡΠ΄Π½ΠΈΠΊΠΎΠ² ΠΌΠΎΠΆΠ΅Ρ Π±ΡΡΡ ΠΏΠΎΠ²ΡΡΠ΅Π½Π° ΡΡΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎΡΡΡ ΡΠΏΡΠ°Π²Π»Π΅Π½ΠΈΡ ΡΠΈΡΠΊΠ°ΠΌΠΈ ΡΠΈΠ½Π°Π½ΡΠΎΠ²ΠΎ-Ρ
ΠΎΠ·ΡΠΉΡΡΠ²Π΅Π½Π½ΠΎΠΉ Π΄Π΅ΡΡΠ΅Π»ΡΠ½ΠΎΡΡΠΈ Π² ΡΡΠ»ΠΎΠ²ΠΈΡΡ
ΠΏΠ°Π½Π΄Π΅ΠΌΠΈΠΈ Π½Π° ΡΡΠΎΠ²Π½Π΅ ΡΠ½ΠΈΠ²Π΅ΡΡΠΈΡΠ΅ΡΠ°. Π ΠΎΡΡ ΡΡΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎΡΡΠΈ ΠΎΠ±Π΅ΡΠΏΠ΅ΡΠΈΠ²Π°Π΅ΡΡΡ ΠΊΠΎΡΡΠ΅ΠΊΡΠΈΠ΅ΠΉ ΠΏΠ»Π°Π½ΠΎΠ² ΠΏΠΎ ΠΌΠΈΠ½ΠΈΠΌΠΈΠ·Π°ΡΠΈΠΈ ΡΠΈΡΠΊΠΎΠ² Ρ ΡΡΠ΅ΡΠΎΠΌ Π½Π΅ΠΎΠ΄Π½ΠΎΡΠΎΠ΄Π½ΠΎΡΡΠΈ Π²Π»ΠΈΡΠ½ΠΈΡ ΠΏΠ°Π½Π΄Π΅ΠΌΠΈΠΈ Π½Π° ΡΠΎΡΡΡΠ΄Π½ΠΈΠΊΠΎΠ² Π² Π·Π°Π²ΠΈΡΠΈΠΌΠΎΡΡΠΈ ΠΎΡ Π²ΠΎΠ·ΡΠ°ΡΡΠ°, Π³Π΅Π½Π΄Π΅ΡΠ°, ΠΏΡΠΈΠ½Π°Π΄Π»Π΅ΠΆΠ½ΠΎΡΡΠΈ ΠΊ ΠΊΠ°ΡΠ΅Π³ΠΎΡΠΈΠΈ ΠΏΠ΅ΡΡΠΎΠ½Π°Π»Π°. ΠΠ»Ρ ΠΈΠ½ΡΠ΅Π³ΡΠ°ΡΠΈΠΈ ΠΈ Π°Π½Π°Π»ΠΈΠ·Π° Π΄Π°Π½Π½ΡΡ
Π°Π²ΡΠΎΡΠ°ΠΌΠΈ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π»ΠΈΡΡ ΠΏΠΎΠ΄Ρ
ΠΎΠ΄Ρ Data Science. ΠΠ° ΠΏΡΠΈΠΌΠ΅ΡΠ΅ Π΄Π°Π½Π½ΡΡ
Π£ΡΠ°Π»ΡΡΠΊΠΎΠ³ΠΎ ΡΠ΅Π΄Π΅ΡΠ°Π»ΡΠ½ΠΎΠ³ΠΎ ΡΠ½ΠΈΠ²Π΅ΡΡΠΈΡΠ΅ΡΠ° ΠΏΠΎΠΊΠ°Π·Π°Π½Π° ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠ²Π½ΠΎΡΡΡ Π°Π½Π°Π»ΠΈΠ·ΠΈΡΡΠ΅ΠΌΡΡ
Π΄Π°Π½Π½ΡΡ
ΠΈ ΠΊΠ°ΠΊΠΈΠ΅ ΡΠΏΡΠ°Π²Π»Π΅Π½ΡΠ΅ΡΠΊΠΈΠ΅ ΡΠ΅ΡΠ΅Π½ΠΈΡ ΠΏΠΎ ΠΌΠΈΠ½ΠΈΠΌΠΈΠ·Π°ΡΠΈΠΈ ΡΠΈΡΠΊΠΎΠ² ΠΌΠΎΠ³ΡΡ Π±ΡΡΡ ΠΏΡΠΈΠ½ΡΡΡ Π½Π° ΠΈΡ
ΠΎΡΠ½ΠΎΠ²Π΅. ΠΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½ ΠΏΠΎΠ΄Ρ
ΠΎΠ΄ ΠΊ ΠΊΠΎΠ»ΠΈΡΠ΅ΡΡΠ²Π΅Π½Π½ΠΎΠΌΡ Π°Π½Π°Π»ΠΈΠ·Ρ Π²ΠΎΠ·Π΄Π΅ΠΉΡΡΠ²ΠΈΡ ΠΏΠ°Π½Π΄Π΅ΠΌΠΈΠΈ Π½Π° ΡΠΎΡΡΡΠ΄Π½ΠΈΠΊΠΎΠ² ΡΡΠΈΠ΄ΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ Π»ΠΈΡΠ°. ΠΡΠΎΠ΄Π΅ΠΌΠΎΠ½ΡΡΡΠΈΡΠΎΠ²Π°Π½Π° ΡΡΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎΡΡΡ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΡ Π΄ΠΈΡΡΠ°Π½ΡΠΈΠΎΠ½Π½ΠΎΠ³ΠΎ ΠΎΠ±ΡΡΠ΅Π½ΠΈΡ Π΄Π»Ρ ΠΏΡΠΎΡΠΈΠ²ΠΎΠ΄Π΅ΠΉΡΡΠ²ΠΈΡ ΠΏΠ°Π½Π΄Π΅ΠΌΠΈΠΈ, ΡΡΠ·Π²ΠΈΠΌΠΎΡΡΡ Π΄Π»Ρ ΠΏΠ°Π½Π΄Π΅ΠΌΠΈΠΈ ΠΎΡΠ΄Π΅Π»ΡΠ½ΡΡ
ΠΊΠ°ΡΠ΅Π³ΠΎΡΠΈΠΉ ΡΠΎΡΡΡΠ΄Π½ΠΈΠΊΠΎΠ², Π³Π΅Π½Π΄Π΅ΡΠ½Π°Ρ ΡΡΡΡΠΊΡΡΡΠ° Π½Π΅ΡΡΡΠ΄ΠΎΡΠΏΠΎΡΠΎΠ±Π½ΠΎΡΡΠΈ. Π’Π΅ΠΎΡΠ΅ΡΠΈΡΠ΅ΡΠΊΠ°Ρ Π·Π½Π°ΡΠΈΠΌΠΎΡΡΡ ΡΠ°Π±ΠΎΡΡ ΡΠΎΡΡΠΎΠΈΡ Π² ΡΠ°Π·ΡΠ°Π±ΠΎΡΠΊΠ΅ ΠΏΠΎΠ΄Ρ
ΠΎΠ΄ΠΎΠ² ΠΊ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΡ ΡΠΈΠ½Π°Π½ΡΠΎΠ²ΠΎΠΉ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈ Π΄Π»Ρ ΡΠΎΠ²Π΅ΡΡΠ΅Π½ΡΡΠ²ΠΎΠ²Π°Π½ΠΈΡ ΡΠΏΡΠ°Π²Π»Π΅Π½ΠΈΡ ΡΠΈΡΠΊΠ°ΠΌΠΈ. ΠΠΎΠ»ΡΡΠ΅Π½Π½Π°Ρ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΡ ΠΌΠΎΠΆΠ΅Ρ Π±ΡΡΡ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½Π° Π½Π° ΠΏΡΠ°ΠΊΡΠΈΠΊΠ΅, Π² ΡΠ°ΡΡΠ½ΠΎΡΡΠΈ, Π΄Π»Ρ ΡΡΠΎΡΠ½Π΅Π½ΠΈΡ ΡΠ°ΡΡΠ΅ΡΠ° ΡΠ΅Π·Π΅ΡΠ²ΠΎΠ², ΡΠΎΠ²Π΅ΡΡΠ΅Π½ΡΡΠ²ΠΎΠ²Π°Π½ΠΈΠ΅ ΡΠ΅Ρ
Π½ΠΈΡΠ΅ΡΠΊΠΈΡ
Π·Π°Π΄Π°Π½ΠΈΠΉ ΠΏΡΠΈ ΡΠ°Π·ΡΠ°Π±ΠΎΡΠΊΠ΅ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΎΠ½Π½ΡΡ
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