From informal to formal: scientific knowledge role transition prediction

Jinqing Yang (), Zhifeng Liu and Yong Huang
Additional contact information
Jinqing Yang: Central China Normal University
Zhifeng Liu: Peking University
Yong Huang: Wuhan University

Scientometrics, 2024, vol. 129, issue 8, No 12, 4909-4935

Abstract: Abstract Comprehending the patterns of knowledge evolution benefits funding agencies, policymakers, and researchers in developing creative ideas. We introduce the notation of scientific knowledge role transition as an evolution from informal to formal. We investigate how different factors affect the role transition of scientific knowledge, considering the two primary levels—transition pace and transition possibility. The interpretive machine learning models are conducted to discover that the Gradient Boosting classifier performs better for predicting transition possibility, and Random Forests regression is the most effective for predicting transition pace. Specifically, knowledge attribute features have a more obvious effect on the transition probability, while knowledge network structure has a greater effect on the transition pace. We further find that knowledge relatedness and citation number have negative effects on knowledge role transition, while adoption frequency, indegree centrality in the knowledge citation network, node number of the egocentric co-occurrence network, and journal impact of scientific knowledge have positive effects. The aforementioned discoveries enhance our comprehension of scientific knowledge evolution patterns and provide insight into the trajectory of scientific and technological advancement.

Keywords: Knowledge evolution; Knowledge role transition; Innovation pace; Innovation possibility (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://link.springer.com/10.1007/s11192-024-05093-1 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:129:y:2024:i:8:d:10.1007_s11192-024-05093-1

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

DOI: 10.1007/s11192-024-05093-1

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 ().