A model for reference list length of scholarly articles

Fatemeh Ghaffari and Mark C. Wilson ()
Additional contact information
Fatemeh Ghaffari: University of Massachusetts Amherst
Mark C. Wilson: University of Massachusetts Amherst

Scientometrics, 2023, vol. 128, issue 9, No 17, 5335-5350

Abstract: Abstract We introduce and analyse a simple probabilistic model of article production and citation behavior that explicitly assumes that there is no decline in citability of a given article over time. It makes predictions about the number and age of items appearing in the reference list of an article. The latter topics have been studied before, but only in the context of data, and to our knowledge no models have been presented. We then perform large-scale analyses of reference list length for a variety of academic disciplines. The results show that our simple model cannot be rejected, and indeed fits the aggregated data on reference lists rather well. Over the last few decades, the relationship between total publications and mean reference list length is linear to a high level of accuracy. Although our model is clearly an oversimplification, it will likely prove useful for further modeling of the scholarly literature. Finally, we connect our work to the large literature on “aging” or “obsolescence” of scholarly publications, and argue that the importance of that area of research is no longer clear, while much of the existing literature is confused and confusing.

Keywords: Bibliometrics; Scientometrics; Growth of scientific literature (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s11192-023-04780-9 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:scient:v:128:y:2023:i:9:d:10.1007_s11192-023-04780-9

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11192

DOI: 10.1007/s11192-023-04780-9

Access Statistics for this article

Scientometrics is currently edited by Wolfgang Glänzel

More articles in Scientometrics from Springer, Akadémiai Kiadó
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().