Revisiting the uniformity and inconsistency of slow-cited papers in science

Takahiro Miura, Kimitaka Asatani and Ichiro Sakata

Journal of Informetrics, 2023, vol. 17, issue 1

Abstract: Quantitative analyses on delayed recognition indicated by slow-cited papers, including delayed papers and durable papers, have long been discussed to reveal why outstanding discoveries remain unnoticed. However, these analyses include contradictory arguments, such as which combinations of knowledge, over-specialization, or transdisciplinary factors have led to undervaluation. We claim that this is because the indicators of delayed recognition are methodologically similar but capture conceptually different phenomena. Subsequently, this paper examined the overlap of 11 slow-cited measures to identify the uniformity and inconsistency of delayed recognition. Consequently, each measure practically obtained different papers as delayed recognition objectively classified into four groups by citation feature clustering, albeit based on similar concepts. Despite the ambiguity, we found that all delayed recognition measures extract papers that are more likely to be single-author projects that make disruptive contributions to more diverse fields without extremely novel nor conventional knowledge combinations that have been gradually awakened, compared to the null models. This result is robust when applying other hyperparameters, research topic-controlled null models, year-controlled null models, and other fields. This strongly indicates that delayed recognition leads to the reconstruction of a new direction of science and contributes to pioneering a revolutionary research topic. The source code for extracting slow-cited papers is available online.11https://github.com/TM82/pyhibernator

Keywords: Slow-cited; Sleeping beauty; Delayed recognition; Citation analysis; Scientometrics; Science of science (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S1751157723000032
Full text for ScienceDirect subscribers only

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:eee:infome:v:17:y:2023:i:1:s1751157723000032

DOI: 10.1016/j.joi.2023.101378

Access Statistics for this article

Journal of Informetrics is currently edited by Leo Egghe

More articles in Journal of Informetrics from Elsevier
Bibliographic data for series maintained by Catherine Liu ().