Measuring coauthors' credit in medicine field-Based on author contribution statement and citation context analysis

The existing credit allocation method of coauthored research paper could not tell the whole story about who did what and the acknowledgment of different parts of the article. When an article is cited, the first author often gets the primary or even full credit, even if the citing paper cites the method part of the article, which is mainly contributed by the second author. This study proposes a context-based author credit (CAC) model to allocate individual credit to coauthors in a multi-authored paper. In the proposed model, coauthor's credit is conceptualized as a directed and weighted connection between citations and contributor roles, where the relationship was decided by citation context. Citation strength was used in the proposed model instead of the number of citing papers which can make the credit of research more precise. The proposed approach can complement existing measures of author credit analysis based on author signature order. In our experiments, the model was validated by fitting to empirical data, a group of highly productive authors' articles and their citing papers, from PLOS Medicine. The results show that CAC model outperforms prior alternatives such as normal, fractional, harmonic counting and author contribution solely based on contribution list in terms of reflecting the specific performance of coauthors. Besides, the CAC model has a certain sensitivity to the contributions of lower-ranked authors, breaking through the restriction of the author's signature order. This paper also provides the new application of this model in author academic evaluation.

Automated summary

The study proposes a context-based author credit (CAC) model to accurately allocate individual credit to coauthors in a multi-authored paper. The model bypasses the limitations of existing author credit analysis methods by considering the relationship between citation strength and contributor roles, and introduces a new application in author academic evaluation.