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Developing and Testing the Forecasting Algorithm for the Technological Revolution Theme through the Analysis of the SCImago JR Scientific Journal Database


Olga Sergeevna Prichina, Victor Dmitrievich Orekhov, Elena Nikolaevna Egorova, Olesya Gennadievna Kukharenko and Alla Viktorovna Blinnikova
Abstract

The algorithm applied to forecast the next technological revolution‟s trends has been studied and tested in this work. For this purpose, using the suggested algorithm, the bibliometric database of the 1999 and 2018 SCImago JR journals has been properly analyzed. It has been shown that the journals No. 2,000 – 10,000 (while the total amount of those journals was17.2 thousand in 1999 and 32 thousand in 2018) make the main contribution to the theme relevance in the SJR rating per their weight according to the Hirsch index and number. In terms of importance, biomedical themes were leading (44.3 %). In 2018, they included such sciences as medicine (25.5 %), genetics (7.3 %), psychiatry (4 %), neuro sciences (2.7 %), agro-bio, and nutrition sciences (4.8 %). In 2018, 34.5 % of such journals were dedicated to technical sciences. In terms of importance, 8.3 % of them referred to computer sciences. However, their maximum referred to the SJR journals no. 5,000 – 20,000, which did not characterize these scientific areas as the leading ones. In general, having analyzed the titles of scientific and technical works, it can be noted that the key areas include the sciences aimed at the growth of human capital: healthcare, education, sociology, etc. The revolutionary technological breakthroughs are most likely in such areas.

Volume 12 | 04-Special Issue

Pages: 712-724

DOI: 10.5373/JARDCS/V12SP4/20201538