Uncited papers in the structure of scientific communication

• Published in: Journal of informetrics 2023-05, Vol.17 (2), p.101391, Article 101391
• Main Authors: Katchanov, Yurij L., Markova, Yulia V., Shmatko, Natalia A.
• Format: Article
• Language: English
• Subjects: Bibliometrics; Citation analysis; INSPIRE; Uncited papers; Uncitness
• ISSN: 1751-1577; 1875-5879
• DOI: 10.1016/j.joi.2023.101391

•The citations of 729,515 articles fit by the generalized extreme-value distribution.•The complexity of references and citations of papers’ quantiles is studied.•The macroscopic model of the citation distribution is proposed. The paper presents an in-depth study of uncited papers. For that, we explore the documents indexed in the INSPIRE database from 1970 to 2015. Uncited articles represent a complex bibliometric environment in which references are generated. The reference lists of uncited papers form a dynamic system partially responsible for the redistribution of scientific impact. Our task is to quantify the detailed structure of citations and references directly in terms of quantiles. We also study the entropy and the statistical complexity measure of references and citations of papers’ quantiles. We introduce a theoretical framework in which citation distribution is considered an asymptotic distribution of the largest values in a sequence of independent identically distributed random variables. We show that this asymptotic distribution is the generalized extreme-value distribution. Furthermore, we empirically demonstrate that the asymptotic behavior of citation distribution is close to (but not quite) the generalized extreme-value distribution.