Predicting scholar potential: a deep learning model on social capital features

Dehu Yin (), Xi Zhang (), Hongke Zhao () and Li Tang ()
Additional contact information
Dehu Yin: Tianjin University
Xi Zhang: Tianjin University
Hongke Zhao: Tianjin University
Li Tang: Fudan University

Scientometrics, 2024, vol. 129, issue 12, No 16, 7879 pages

Abstract: Abstract Identifying scholars with potentials early in their careers is critical for informed evaluations, effective allocation of funding, and tenure decisions, which in turn propel advancements in science and technology. This paper investigates the impact of social capital features on the identification of such scholars. Utilizing a comprehensive dataset spanning from 1991 to 2020, extracted from the Microsoft Academic Knowledge Graph, we analyze the novelty values of 56,568 scholars’ future publications using disruption index. We identify potential scholars as those within the top 1% based on these values. Our approach involves extracting nine key features of structural, relational, and cognitive capital from the dynamic co-authorship networks of these scholars during their early career stages. The influence of these features on scholar identification is assessed through ablation experiments using an LSTM-based predictive model. Our findings underscore the critical importance of cognitive capital features in the identification process. Furthermore, the integration of structural and relational capital features markedly enhances the model’s predictive accuracy, achieving significant improvements in precision metrics. Notably, relational capital features demonstrate a greater influence than structural features in predicting scholar potentials. These results provide essential insights and practical implications for strategies aimed at recognizing and fostering outstanding academic talent.

Keywords: Potential scholars; Social capital features; Novelty; Deep learning; Early careers (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s11192-024-05196-9 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:129:y:2024:i:12:d:10.1007_s11192-024-05196-9

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

DOI: 10.1007/s11192-024-05196-9

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 ().