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<p>Editorial boards (EBs) play a fundamental role in guiding journals’ review process, determining which manuscripts are published and which are rejected. Consequently, they may be considered “gatekeepers of knowledge,” shaping the direction of a discipline’s scholarship. This study provides a descriptive network analysis of the membership of 98 EBs listed in the Web of Science Master Journal List of Communication journals, encompassing 4,999 individuals, 1,295 institutions, and 76 countries, employing a multi-level network analysis to identify the central and potentially influential nations, institutions, and individuals, and journals in the field. Community detection using the Leiden algorithm identified 20 distinct communities at the individual level (modularity = 0.622), revealing meaningful subgroup structure within the field. The results reveal that the United States (US) and other English-speaking nations, along with American universities and certain individuals are more central, and thus, more influential. Additionally, more prestigious journals, those ranked higher by the Journal Citation Reports (JCR), show greater centrality across all levels. However, the degree of centrality varies across journals depending on the level of analysis.</p>

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Revealing academic gatekeepers in communication journals: a multilevel network analysis of scholars, institutions, and nation-states on editorial boards

  • 期刊论文

  • 发表日期：

    2026年7月8日

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  • George A. Barnett,
  • Pablo M. Flores,
  • Camille Velasco Lim,
  • Han Woo Park

摘要

Editorial boards (EBs) play a fundamental role in guiding journals’ review process, determining which manuscripts are published and which are rejected. Consequently, they may be considered “gatekeepers of knowledge,” shaping the direction of a discipline’s scholarship. This study provides a descriptive network analysis of the membership of 98 EBs listed in the Web of Science Master Journal List of Communication journals, encompassing 4,999 individuals, 1,295 institutions, and 76 countries, employing a multi-level network analysis to identify the central and potentially influential nations, institutions, and individuals, and journals in the field. Community detection using the Leiden algorithm identified 20 distinct communities at the individual level (modularity = 0.622), revealing meaningful subgroup structure within the field. The results reveal that the United States (US) and other English-speaking nations, along with American universities and certain individuals are more central, and thus, more influential. Additionally, more prestigious journals, those ranked higher by the Journal Citation Reports (JCR), show greater centrality across all levels. However, the degree of centrality varies across journals depending on the level of analysis.