The physics of journal self-citation
Year: 2025
Type: article
Abstract: ABSTRACT Purpose This study explores the implications of low journal self-citation rate (LJSCR). While some interpret LJSCR as a sign that a journal’s authors do not cite each other’s work, others see it as a mark of prestige, reflecting greater recognition from outside the journal. We argue that these perspectives are not contradictory: low self-citation can be prestigious precisely because it reflects low self-readership. Design/methodology/approach We analyze the physics and mathematics of journal self-citation. Our findings show that the self-citation rate (i) increases with journal market share, (ii) approaches a well-defined upper bound, (iii) this upper bound remains significantly below unity, and (iv) without a minimum level of market share, self-citation is virtually absent. Here, market share refers to the proportion of a journal’s publications relative to its Web of Science (WOS) subject category. To test our analysis, we examine 61 journal-years of data from three major hybrid fluid dynamics journals: Journal of Fluid Mechanics, Physical Review Fluids , and Physics of Fluids . Findings We identify a consistent relationship between journal self-citation and market share. A striking result is the mathematical analogy we establish between journal self-citation behavior and the concentration of intermediates in consecutive irreversible unimolecular chemical reactions. We also observe that creating specialized subdisciplinary journals (“twigging”) can reduce self-citation rates by narrowing scope. Research limitations The study is limited to fluid dynamics journals. Broader validation across disciplines is needed. Practical implications Editors and publishers can apply these insights to interpret citation metrics and assess the visibility and readership of their journals. Originality/value This work redefines LJSCR as a counterintuitive metric—one that may reflect both low author engagement and high external impact. It introduces a novel physics-based model to understand citation behavior across journals.
Language: English
Subfield: Statistics, Probability and Uncertainty
Field: Decision Sciences
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W4411899243