An author credit allocation method with improved distinguishability and robustness
Tao Jia
Profil Orcid 
Kategoria artykułu: Research Paper
Data publikacji: 25 sie 2023
Zakres stron: 15 - 46
Otrzymano: 13 maj 2023
Przyjęty: 28 maj 2023
DOI: https://doi.org/10.2478/jdis-2023-0016
Słowa kluczowe
© 2023 Yang Li et al., published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.
Purpose
The purpose of this study is to propose an improved credit allocation method that makes the leading author of the paper more distinguishable and makes the deification more robust under malicious manipulations.
Design/methodology/approach
We utilize a modified
Finding
Compared with the state-of-the-art methods, NCCAS gives the most accurate prediction of Nobel laureates. Furthermore, the leading author of the paper identified by NCCAS is more distinguishable compared with other co-authors. The results by NCCAS are also more robust to malicious manipulation. Finally, we perform ablation studies to show the contribution of different components in our methods.
Research limitations
Due to limited ground truth on the true leading author of a work, the accuracy of NCCAS and other related methods can only be tested in Nobel Physics Prize-winning papers.
Practical implications
NCCAS is successfully applied to a large number of publications, demonstrating its potential in analyzing the relationship between the contribution and the recognition of authors with different by-line orders.
Originality/value
Compared with existing methods, NCCAS not only identifies the leading author of a paper more accurately, but also makes the deification more distinguishable and more robust, providing a new tool for related studies.