Evaluating scientists by citation and disruption of their representative works

Wang, Ruijie; Zhou, Yuhao; Zeng, An

doi:10.1007/s11192-023-04631-7

Cited by 13 publications

(4 citation statements)

References 51 publications

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“…The average impact factors of the journals of the papers published by the first author before the evaluated paper < 1 1611 [1,2] 681…”

Section: < 20 2202mentioning

confidence: 99%

“…In recent years, universities and research institutes have frequently adopted an evaluation system based on representative academic papers for research management and decision-making, such as recruitment, promotion, awarding, and funding [1]. This evaluation system requires researchers to submit the papers they have published from the past three to five years, which are then evaluated based on their originality, academic contribution, and academic influence.…”

Section: Introductionmentioning

confidence: 99%

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An Early Evaluation of the Long-Term Influence of Academic Papers Based on Machine Learning Algorithms

Qiu,

Han

2024

IEEE Access

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Evaluating the long-term influence of academic papers in the early stage after publication is crucial for research management and decision-making. Although the total number of citations has been considered as a valid indicator to measure the academic influence of papers, there are still some bottleneck problems in predicting future citation counts. Firstly, it remains a challenging task to select the reasonable features due to the diversity of model features. Secondly, some prediction models are overly complex and their defects may cause prediction bias or even errors. Finally, the most important thing is that very few models possess long-term predictive ability. This paper proposes a prediction model that can provide an early evaluation of the long-term citations, or the long-term influence, of academic papers. The model uses features available immediately after publication or after a certain period of time. Taking academic papers in Information Science and Library Science as our experimental subject, we train the model using three machine learning algorithms and find that the artificial neural network performs satisfactorily. Our findings may offer guidance for research management and decision-making.

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“…The average impact factors of the journals of the papers published by the first author before the evaluated paper < 1 1611 [1,2] 681…”

Section: < 20 2202mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Early Evaluation of the Long-Term Influence of Academic Papers Based on Machine Learning Algorithms

Qiu,

Han

2024

IEEE Access

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“…To measure the impact of scientific papers and scientists, numerous citation-based indicators, such as the h-index (Hirsch, 2005;Schubert, 2009) and the g-index (Egghe, 2006), have been proposed to measure the impact of scientific papers and scientists over the years. However, traditional metrics have been criticized for their inconsistency (Brito & Navarro, 2021;Waltman & Van Eck, 2012), especially in measuring milestone research and top scientists' impact (Bornmann & Tekles, 2021;Herrmannova et al, 2018;Wang et al, 2023). These issues have led scientists to resist traditional research evaluation procedures through the San Francisco Declaration (Cagan, 2013) and the Leiden Manifesto (Hicks et al, 2015), and to call for more comprehensive metrics.…”

Section: Introductionmentioning

confidence: 99%

Apex Index: A Hit-based Metric for Measuring the Impact of Papers and Scientists

Yang

Zhang

Wang

et al. 2023

27th International Conference on Science, Technology and Innovation Indicators (STI 2023)

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In this study, we introduce a novel metric called the Apex index, which is a hit-based measure for assessing the impact of scientific publications and scientists. The basic principle of the Apex index involves quantifying the number of hit papers that cite the focal paper. Specifically, we identify the top 1% most highly cited papers in a given field and year as the hit papers. We then calculate the Apex index for all publications in the MAG database, which contains approximately 200 million documents. Our study reveals that Nobel Prize-winning papers display a higher Apex index compared to other papers, and Nobel laureates exhibit a higher Apex index than their peers. Moreover, we demonstrate that the Apex index has a higher convergent validity in evaluating scientists and identifying laureates. Overall, the Apex index presents a valuable and effective tool for assessing the impact of scientific publications and researchers.

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“…Despite the widespread use of disruptive and consolidating impact concepts across various fields (Bornmann & Tekles, 2021; Leydesdorff & Bornmann, 2021; Ruan et al, 2021; Wang et al, 2023), the differences between DCP and CCP of scientific breakthroughs have not been adequately discussed. Therefore, this study aims to address this gap by examining the difference between DCP and CCP of scientific breakthroughs represented by Nobel‐winning papers.…”

Section: Introductionmentioning

confidence: 99%

Untangle the Characteristics of Disruptive and Consolidating Citations of Nobel‐winning Papers

Yang,

Zhao,

Wang

et al. 2023

Proceedings of the Association for Information Science and Tech

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Scientific breakthroughs have the potential to revolutionize the course of research and shape the trajectory of scientific knowledge. This study investigates the characteristics of Disruptive Citing Papers (DCP) and Consolidating Citing Papers (CCP) associated with Nobel‐winning scientific breakthroughs, aiming to provide insights into the mechanisms of knowledge creation and dissemination. By analyzing a dataset of Nobel‐winning papers and their citation networks, we find that Nobel‐winning papers tend to attract a higher proportion of DCP compared to CCP. However, CCP exhibit a higher impact, as evidenced by their citation counts and likelihood of becoming hit papers. Furthermore, DCP are associated with larger research teams, highlighting the collaborative nature of disruptive research, while CCP employ a higher degree of professional language style characterized by shorter titles and specialized jargon. These findings deepen our understanding of the role played by disruptive and consolidating impact in scientific breakthroughs, shedding light on the dynamics of knowledge creation and dissemination in the scientific community. This research contributes to the broader understanding of scientific progress and provides valuable insights for researchers, policymakers, and stakeholders in the scientific ecosystem.

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Evaluating scientists by citation and disruption of their representative works

Cited by 13 publications

References 51 publications

An Early Evaluation of the Long-Term Influence of Academic Papers Based on Machine Learning Algorithms

An Early Evaluation of the Long-Term Influence of Academic Papers Based on Machine Learning Algorithms

Apex Index: A Hit-based Metric for Measuring the Impact of Papers and Scientists

Untangle the Characteristics of Disruptive and Consolidating Citations of Nobel‐winning Papers

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