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<p>Journal impact measures are expected to demonstrate temporal stability, as journal quality typically remains consistent year-over-year. However, the COVID-19 pandemic has disrupted journal evaluation systems through an unprecedented surge in highly-cited publications. While conventional metrics like the Journal Impact Factor have shown high sensitivity to these citation patterns, the CAS Journal Ranking (CJR) system has demonstrated notable stability. This study compares three metrics—JIF, Category Normalized Citation Impact, and Field Normalized Citation Success Index (FNCSI, indicator used in CJR)—under different classification systems. We find that combining FNCSI with the CWTS paper-level classification system provides superior ranking stability. This robustness stems from FNCSI’s ability to moderate the influence of highly cited papers and paper-level system’s effective clustering of COVID-19 research.</p>

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Addressing the challenges in journal evaluation during the “covidization” of scientific research era : insights from the CAS journal ranking

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年3月13日

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  • Yahui Liu,
  • Liying Yang,
  • Jiandong Zhang,
  • Zhesi Shen

摘要

Journal impact measures are expected to demonstrate temporal stability, as journal quality typically remains consistent year-over-year. However, the COVID-19 pandemic has disrupted journal evaluation systems through an unprecedented surge in highly-cited publications. While conventional metrics like the Journal Impact Factor have shown high sensitivity to these citation patterns, the CAS Journal Ranking (CJR) system has demonstrated notable stability. This study compares three metrics—JIF, Category Normalized Citation Impact, and Field Normalized Citation Success Index (FNCSI, indicator used in CJR)—under different classification systems. We find that combining FNCSI with the CWTS paper-level classification system provides superior ranking stability. This robustness stems from FNCSI’s ability to moderate the influence of highly cited papers and paper-level system’s effective clustering of COVID-19 research.