Abstract
The problem of the substantiation of the optimal composition of indicators intended for assessing existing and predicting new scientific achievements is considered. The types of indicators that are commonly used in the management of scientific achievements are systematized. A differentiated approach to the selection of such indicators is proposed, depending on the sections of knowledge to which they relate. In addition to the scientometric parameters of knowledge sections, the factors that influence the formation of sets of indicators are listed. The development of a quantitative model of the ratio of types of indicators in their sets based on measures of sets and binary relations of order over numbers is presented. Within the framework of this model, an explanation is given for the prevalence of expert indicators. The decision on the composition of the indicator sets is made based on heuristic rules. An example of finding the optimal ratio of the types of indicators for predicting achievements in the natural sciences and assessing the results achieved in the humanities is given.
Similar content being viewed by others
REFERENCES
Syuntyurenko, O.V., Funding for fundamental research: The conceptual image of a decision support system using methods of scientometrics and data analysis, Inf. Ee Primeneniya, 2018, vol. 12, no. 1, pp. 118–127.
Khoroshevskii, V.F. and Efimenko, I.V., Semantic technologies in scientometrics: Objectives, problems, solutions, and prospects, in Kognitivno-semioticheskie aspekty modelirovaniya v gumanitarnoi sfere (Cognitive-Semiotic Aspects of Modeling in the Humanitarian Sphere), Kazan: Izd. Akad. Nauk, 2017, pp. 222–266.
Markusova, V., Kotel’nikova, N., Zolotova, A., and Shukhaeva, A., Promising areas of scientific research: Global and domestic trends in the SCI-E database, 2009 and 2015, Inf. Innovatsii, 2017, no. S1, pp. 111–118.
Filatova, T.E. and Ponomarev, V.V., The “black swan” theory, Materialy XVI mezhvuzovskoi nauchno-tekhnicheskoi konferentsii “Novye tekhnologii v uchebnom protsesse i proizvodstve” (Proc. XVI Interuniversity Scientific and Technical Conference “New Technologies in the Educational Process and Production”), Ryazan, 2018, pp. 486–488.
Hicks, D., Wouters, P., Waltman, L., De Rijcke, S., and Rafols, I., Bibliometrics: The Leiden Manifesto for research metrics, Nature, 2015, vol. 520, no. 7548, pp. 429–431.
Lazar, M.G., Plagiarism in scientific communications of the modern era, Uch. Zap. Ross. Gos. Gidrometeorol. Univ., 2019, no. 56, pp. 166–175.
Vinogradova, T.V., Dobrosovestnost’ v nauchnykh issledovaniyakh: Analit. obzor (Integrity in Scientific Research: Analytical Overview), Moscow: INION RAN, 2017.
Sidel’nikov, Yu.V., Sistemnyi analiz ekspertnogo prognozirovaniya (System Analysis of Expert Forecasting), Moscow: MAI, 2007.
Methodology for Calculating the Qualitative Indicator of the State Assignment “Comprehensive Score for Publication Performance” for Scientific Organizations Subordinate to the Ministry of Science and Higher Education of the Russian Federation for 2020, 2020. https://minobrnauki.gov.ru/documents/?ELEMENT_ID=24754&sphrase_id=31318.
Vinogradova, T.V., Bibliometrics and social sciences are not compatible?, Naukoved. Issled., 2016, no. 2016, pp. 90–106.
Malashuk, N.M. and Pavlova, E.A., Problems and methods of evaluating the effectiveness of scientific research, in Al’manakh nauchnykh rabot molodykh uchenykh Universiteta informatsionnykh tekhnologii, mekhaniki i optiki (Almanac of Scientific Works of Young Scientists of the University of Information Technologies, Mechanics, and Optics), 2017, pp. 173–177.
Feigel’man, M.V. and Tsirlina, G.A., Bibliometric heat as a consequence of the lack of scientific expertise, Upr. Bol’shimi Sist.: Sb. Tr., 2013, no. 44, pp. 335–342.
Reia, S.M. and Fontanari, J.F., Wisdom of crowds: Much ado about nothing, arXiv, 2020. https://arxiv.org/pdf/2008.01485.pdf.
Golubkov, E.P., Metody prinyatiya upravlencheskikh reshenii (Methods for Making Management Decisions), Moscow: Yurait, 2020, part 2.
Mikhailov, O.V. and Aristov, I.V., “Hybrid” Hirsch indices in the assessment of scientific activity, Naukoved. Issled., 2015, no. 2015, pp. 110–116.
Tsai, C.F., Hu, Y.H., and Ke, S.W.G., A Borda count approach to combine subjective and objective based MIS journal rankings, Online Inf. Rev., 2014, vol. 38, no. 4, pp. 469–483.
Sakulin, S.A. and Alfimtsev, A.N., On the problem of practical application of fuzzy measures and the Choquet integral, Vestn. Mosk. Gos. Tekh. Univ. im. N.E. Baumana, Ser. Priborostr., 2012, no. 1, pp. 55–63.
Bornmann, L. and Hug, S., Bibliometrics-based heuristics: What is their definition and how can they be studied? - Research note, Prof. Inf., 2020, vol. 29, no. 4. https://doi.org/10.3145/epi.2020.jul.20
Funding
This work was carried out as part of a study on the topic 0003-2019-0001 of the State Assignment of VINITI RAS and with the support of the Russian Foundation for Basic Research (RFBR project No. 20-07-00014).
Author information
Authors and Affiliations
Corresponding author
About this article
Cite this article
Kalachikhin, P.A. The Rationale of Indicators for the Management of Scientific Achievements. Autom. Doc. Math. Linguist. 55, 46–53 (2021). https://doi.org/10.3103/S0005105521020035
Received:
Published:
Issue Date:
DOI: https://doi.org/10.3103/S0005105521020035