Quantifying hierarchy in scientific teams

• URL: http://arxiv.org/abs/2210.05852v1
• Date: Wed, 12 Oct 2022 01:28:25 GMT
• Title: Quantifying hierarchy in scientific teams
• Authors: Fengli Xu, Lingfei Wu, James A. Evans
• Abstract summary: This paper provides a detailed description of the data collection and machine learning model used in our recent PNAS paper "Flat Teams Drive Scientific Innovation" We discuss how the features of scientific publication can be used to estimate the implicit hierarchy in the corresponding author teams.
• Score: 42.444263246116485
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper provides a detailed description of the data collection and machine learning model used in our recent PNAS paper "Flat Teams Drive Scientific Innovation" Xu et al. [2022a]. Here, we discuss how the features of scientific publication can be used to estimate the implicit hierarchy in the corresponding author teams. Besides, we also describe the method of evaluating the impact of team hierarchy on scientific outputs. More details will be updated in this article continuously. Raw data and Readme document can be accessed in this GitHub repository Xu et al. [2022b].

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