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Abstract

Peer review is essential for advancing scientific research, but there are long-standing concerns that authors' prestige or other characteristics can bias reviewers. Double-blind peer review has been proposed as a way to reduce reviewer bias, but the evidence for its effectiveness is limited and mixed. Here, we examine the effects of double-blind peer review by analyzing the review files of 5,027 papers submitted to a top computer science conference that changed its reviewing format from single- to double-blind in 2018. First, we find that the scores given to the most prestigious authors significantly decreased after switching to double-blind review. However, because many of these papers were above the threshold for acceptance, the change did not affect paper acceptance significantly. Second, the inter-reviewer disagreement increased significantly in the double-blind format. Third, papers rejected in the single-blind format are cited more than those rejected under double-blind, suggesting that double-blind review better excludes poorer quality papers. Lastly, an apparently unrelated change in the rating scale from 10 to 4 points likely reduced prestige bias significantly such that papers' acceptance was affected. These results support the effectiveness of double-blind review in reducing biases, while opening new research directions on the impact of peer-review formats.

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Abstract

Power dynamics influence every aspect of scientific collaboration. Team power dynamics can be measured by team power level and team power hierarchy. Team power level is conceptualized as the average level of the possession of resources, expertise, or decision-making authorities of a team. Team power hierarchy represents the vertical differences of the possessions of resources in a team. In Science of Science, few studies have looked at scientific collaboration from the perspective of team power dynamics. This research examines how team power dynamics affect team impact to fill the research gap. In this research, all coauthors of one publication are treated as one team. Team power level and team power hierarchy of one team are measured by the mean and Gini index of career age of coauthors in this team. Team impact is quantified by citations of a paper authored by this team. By analyzing over 7.7 million teams from Science (e.g., Computer Science, Physics), Social Sciences (e.g., Sociology, Library & Information Science), and Arts & Humanities (e.g., Art), we find that flat team structure is associated with higher team impact, especially when teams have high team power level. These findings have been repeated in all five disciplines except Art, and are consistent in various types of teams from Computer Science including teams from industry or academia, teams with different gender groups, teams with geographical contrast, and teams with distinct size.