Scientific misconduct responsibility attribution: An empirical study on byline position and team identity in Chinese medical papers

OBJECTIVE

The primary objective of this inquiry was to explore the nexus between authorship attribution in medical literature and accountability for scientific impropriety while assessing the influence of authorial multiplicity on the severity of sanctions imposed.

METHODS

Probit regression models were employed to scrutinize the impact of authorship on assuming accountability for scientific misconduct, and unordered multinomial logistic regression models were used to examine the influence of authorship and the number of bylines on the severity of punitive measures.

RESULTS

First authors and corresponding authors were significantly more likely to be liable for scientific misconduct than other authors and were more likely to be penalized particularly severely. Furthermore, a negative correlation was observed between the number of authors' affiliations and the severity of punitive measures.

CONCLUSION

Authorship exerts a pronounced influence on the attribution of accountability in scientific research misconduct, particularly evident in the heightened risk of severe penalties confronting first and corresponding authors owing to their principal roles. Hence, scientific research institutions and journals must delineate authorship specifications meticulously, ascertain authors' contributions judiciously, bolster initiatives aimed at fostering scientific research integrity, and uphold an environment conducive for robust scientific inquiry.

Research Misconduct Investigations in China's Science Funding System

As stewards of public money, government funding agencies have the obligation and responsibility to uphold the integrity of funded research. Despite an increasing amount of empirical studies examining research-related misconduct, a majority of these studies focus on retracted publications. How agencies spot funding-relevant wrongdoing and what sanctions the offenders face remain largely unexplored. This is particularly true for public funding agencies in emerging science powers. To amend this oversight, we retrieved and analyzed all publicized investigation results from China's largest basic research funding agency over the period from 2005 to 2021. Our findings reveal that both the "police patrol" and "fire alarm" approaches are used to identify misconduct and deter funding-related fraud in China. The principal triggers for investigations are journal article retractions, whistleblowing, and plagiarism detection software. Among the six funding-related misconduct types publicized and punished, the top three are: (1) fraudulent papers, (2) information fabrication and/or falsification in the research proposal, and (3) proposal plagiarism. The most common administrative sanctions are debarment and reclamation of grants. This article argues that more systematic research and cooperation among stakeholders is needed to cultivate research integrity in emerging science powers like China. Specific training and education should be provided for young scientists to help them avoid the pitfall of academic misconduct.

The rank boost by inconsistency in university rankings: Evidence from 14 rankings of Chinese universities

University ranking has become an important indicator for prospective students, job recruiters, and government administrators. The fact that a university rarely has the same position in different rankings motivates us to ask: To what extent could a university’s best rank deviate from its “true” position? Here we focus on 14 rankings of Chinese universities. We find that a university’s rank in different rankings is not consistent. However, the relative positions for a particular set of universities are more similar. The increased similarity is not distributed uniformly among all rankings. Instead, the 14 rankings demonstrate four clusters where rankings are more similar inside the cluster than outside. We find that a university’s best rank strongly correlates with its consensus rank, which is, on average, 38% higher (towards the top). Therefore, the best rank usually advertised by a university adequately reflects the collective opinion of experts. We can trust it, but with a discount. With the best rank and proportionality relationship, a university’s consensus rank can be estimated with reasonable accuracy. Our work not only reveals previously unknown patterns in university rankings but also introduces a set of tools that can be readily applied to future studies.