Author credit‐assignment schemas: A comparison and analysis

Abstract

Credit assignment to multiple authors of a publication is a challenging task owing to the conventions followed within different areas of research. In this study, we present a review of different author credit-assignment schemas, which are designed mainly based on author position and the total number of coauthors on the publication. We implemented, tested, and classified 15 author credit-assignment schemas into 3 types: linear, curve, and "other" assignment schemas. Further investigation and analysis revealed that most of the methods provide reasonable credit-assignment results, even though the credit-assignment distribution approaches are quite different among different types. The evaluation of each schema based on PubMed articles published in 2013 shows that there exist positive correlations among different schemas and that the similarity of creditassignment distributions can be derived from the similar design principles that stress the number of coauthors or the author position, or consider both. We provide a summary about the features of each credit-assignment schema to facilitate the selection of the appropriate one, depending on the different conditions required to meet diverse needs.

Understanding success through the diversity of collaborators and the milestone of career

“…Essentially, an author with more collaborators has a higher level of sociability, which to a large extent helps them achieve success. Our findings suggest that high‐impact authors tend to pursue diverse‐topic collaborations, which confirms the conclusions of Schaltegger et al () and Xu, Ding, Song, and Chambers ().…”

Author Credit for Transdisciplinary Collaboration

“…Many other reasonable credit-assignment schemas have been developed which allocate credit according to the number of coauthors, their ranks, or both. In our previous study [ 12 ], 15 author credit-assignment schemas were divided into three general categories (i.e., linear, curve, and other) according to their coauthor credit-distribution patterns. The distribution of linear credit-assignment schemas is represented by a straight line, with different slopes for different schemas.…”

“…However, most current author credit-assignment schemas do not take transdisciplinary collaboration into account. Previous studies focused on credit-assignment schemas among coauthors of a publication based on author rank, number of coauthors, coauthor role (e.g., first author, corresponding author), or number of citations [ 12 ].…”

The value and credits of n-authors publications

“…However, we point out that publications with large number of co-authors tend to be specific and very particular (Cronin, 2001). For instance, Xu et al (2016) point out that most credit allocation methods fail when there are many co-authors and that we need specific methods to deal with hyper-authorship, which is understood as ten or more co-authors (Liu and Fang, 2012;Tscharntke et al, 2007).…”

“…Other counting methods and procedures have been proposed (Abramo et al, 2013;Assimakis and Adam, 2010;Kim and Diesner, 2014;Liu and Fang, 2012;Lukovits and Vinkler, 1995;Trueba and Guerrero, 2004). All these methods are based on some intuitively correct argument, which makes it difficult to compare them or to claim that one method is superior to the others (Kim and Kim, 2015;Xu et al, 2016). This paper proposes a practical and simple method to compute the expected value of an n-authors publication that takes into consideration the potential added value induced by collaboration in contexts in which there is no prior or ex-ante information about the publication's potential merits or scientific impact.…”