Abstract. The article discusses a conceptual approach to solving the problem of assessing the quality of knowledge transfer in a network of interactions between members of scientific and technical communities that are carriers of different paradigms. Developed within the framework of this study, the models, concepts and mathematical apparatus can be applied in various areas of the modern digital economy, because interaction in it is of a network nature, different paradigms and languages of the description of subject areas are used. The results of the study are pronounced interdisciplinary. Keywords: digital economy, quality of knowledge transfer, thematic modeling, paradigm modeling, knowledge transfer model PP. 96-104. DOI: 10.14357/20790279190109 References
1. Alaeddini A., Hong S.H. A multi-way multitask learning approach for multinomial logistic regression: An application in joint prediction of appointment miss-opportunities across multiple clinics // Methods of Information in Medicine, 2017, 56(4), pp. 294-307. 2. Martin O., Fink A., Richter M. Communication skills with e-learning // Medizinische Welt, 2017, 68(1), pp. 11-16. 3. Qian X.-D., Xia J., Liu W., Tsai S.-B. An empirical study on sustainable innovation academic entrepreneurship process model // Sustainability (Switzerland), 2018, 10(6), pp. 1974. 4. Wang C., Zuo M., An X. Differential influences of perceived organizational factors on younger employees’ participation in offline and online intergenerational knowledge transfer // International Journal of Information Management, 2017, 37(6), pp. 650-663. 5. Hsiao Y.-C., Chen C.-J., Choi Y.R. The innovation and economic consequences of knowledge spillovers: fit between exploration and exploitation capabilities, knowledge attributes, and transfer mechanisms // Technology Analysis and Strategic Management, 2017, 29(8), pp. 872-885. 6. Bailey M. Absorptive Capacity, International Business Knowledge Transfer, and Local Adaptation // Australian Economic History Review, 2017, 57(2), pp. 194-216. 7. Morioka S.N., Bolis I., Evans S., Carvalho M.M. Transforming sustainability challenges into competitive advantage: Multiple case studies kaleidoscope converging into sustainable business models // Journal of Cleaner Production, 2018, №167, pp. 723-738. 8. AghaeiRad A., Chen N., Ribeiro B. Improve credit scoring using transfer of learned knowledge from self-organizing map // Neural Computing and Applications, 2017, 28(6), pp. 1329-1342. 9. Mihalcea. Knowledge transfer in organization of future // International Multidisciplinary Scientific GeoConference Surveying Geology and Mining Ecology Management, SGEM, 2017, 17(53), pp. 555-562. 10. Andrea Č., Mária R., Tatiana Č. Knowledge Transfer Model and Spin-off Company set up in Significant Academic Centres in Taiwan // Procedia Engineering, 2017, 192, pp. 86-91. 11. Nosulenko V.N., Terekhin V.A. Peredacha znaniy: obzor osnovnyh modeley i tekhnologiy [transfer of knowledge: review of basic models and technologies] // Eksperimental’naya psyhologiya. M.: Moskovskiy gosudarstvenniy psyhologopedagogicheskiy universitet [ Experimental Psychology. M.: Moscow State University of Psychology and Education], 2017. Part. 10. № 4. P. 96-115. 12. Konovalova V.G. Ot peredachi znaniy k priobreteniyu opyta: razvitiye praktiko-orientirovannih modeley vysshego obrazovaniya [from knowledge transfer to acquisition of experience: development of practiceoriented models of higher education] // Aktual’niye voprosy upravleniya personalom I ekonomoki truda materialy III Vserossiyskoy nauchnoprakticheskoy konferencii. Gosudarstvenniy universitet upravleniya, Nacional’niy soyuz “Upravleniye personalom”. M.: Gosudarstvenniy universitet upravleniya [Current issues of personnel management and labor economics materials of the III All-Russian Scientific Practical Conference. State University of Management, National Union "Personnel Management”. M.: State University of Management], 2017. P. 104-109. 13. Rukovishnikova N.A. Kontekst kak komponent diskursivno-ontologicheskoy modeli peredachi znaniy [context as a component of discourseontological model of knowledge transfer] // cifrovaya gumanitaristika: resursy, motody, issledovaniya. Materialy Mezhdunarodnoy nauchnoy konferencii. V 2-h chastyah. Perm’: Permskiy gosudarstvenniy nocional’niy issledovatel’skiy universitet [ Digital Humanitaristics: Resources, Methods, Research Materials of the International Scientific Conference. In 2 parts. Perm: Perm State National Research University], 2017. P. 156-159. 14. Pokrovskaya N.N. Peredacha znaniy v virtual’noy kommunikacii: dinamika kompetentnosti v cifrovoy ekonomike [Transfer Of Knowledge In Virtual Communication: Dynamics Of Competence In A Digital Economy] // Rossiya v globalnom mire. S-Pb: Federalnoye gosudarstvennoye avtonomnoe obrazovatel’noye uchrezhdeniye vysshego obrazovaniya “Sankt-Peterburgskiy politehnicheskiy universitet Petra Velikogo” [Russia in the global world. St. Petersburg: Federal State Autonomous Educational Institution of Higher Education “St. Petersburg Polytechnic University of Peter the Great”], 2016. № 8 (31). P. 596-605. 15. Burova N.V., Pokrovskaya N.N. Setevizaciya ekonomiki i seteviye tekhnologii peredachi znaniy [Networking Of Economics And Network Technologies Of Knowledge Transfer] // Rossiya I Sankt-Peterburg: ekonomika i obrazovaniye v XXI veke. S-Pb: Sankt-Peterburgskiy gosudarstvenniy ekonomicheskiy universitet [ Russia and St. Petersburg: economics and education in the XXI century. St. Petersburg: St. Petersburg State University of Economics], 2016. P. 212-213. 16. Pavlov M.Yu. Kreativnaya ekonomika: kak ostanovit utratu znaniy [Creative Economy: How To Stop A Loss Of Knowledge] // Sociologicheskiye issledovaniya. M.: Federalnoya gosudarstvennoye unitarnoye predpriyatiye “Akademicheskiy nauchno-izdatel’skiy, proizvodstvenno-poligraficheskiy I knigiraspredelitel’niy centr “Nauka” [Sociological studies. M.: Federal State Unitary Enterprise “Academic Scientific Publishing, Production and Printing and Book Distribution Center “Science”], 2018. № 3. P. 144-148. 17. Krhumalev V.V., Gordienko V.N., Mochenov A.D. Osnovi postroeniya telekommunikazionnyh system I setey [Basics of building telecommunication systems and networks] / Pod red. V.V. Krhumaleva i V.N. Gordienko. - M.: Goryachaya liniya - Telekom [Ed. V.V. Krhumaleva and V.N. Gordienko. - M.: Hot line – Telecom], 2004. - 510 p. 18. Zyuko A.G. and others. Nejriya peredachi signalov. M.: Radio I svyaz’ [Theory of signal transmission. M .: Radio and communication], 2006. - 312 p. 19. Held G. Technologii peredachi dannyh [Data Transmission Technologies]. M: 2003. - 720 p. 20. Blei D.M., Ng A.Y., Jordan M.I. Latent Dirichlet allocation // Journal of Machine Learning Research, 2003, Vol.3, pp. 993–1022. 21. Akimova G.P., Solov’ev A.V., Yanishevskiy I.M. 2006. Metodologiya otsenki effektivnosti ierarkhicheskikh informatsionnykh system. [Methodology for assessing the effectiveness of hierarchical information systems]. T.23, S.48–66. [A systematic approach to information management. Proceedings of the ISA RAS.]. 22. Akimova G.P., Solov’ev A.V., Pashkina E.V. 2010. Analiz otsenki effektivnosti ierarkhicheskoy territorial’no raspredelennoy sistemy na primere GAS «Vybory». [Analysis of the assessment of the effectiveness of a hierarchical territoriallydistributed system on the example of GAS «Vybory”]. Obrabotka izobrazheniy i analiz dannykh. Trudy Instituta sistemnogo analiza RAN, 2010, t.58, S.27-42. [Image processing and data analysis. Proceedings of the Institute of System Analysis, Russian Academy of Sciences].
|