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Evaluation of h-index, its variants and extensions based on publication age & citation intensity in civil engineering

Author

Listed:
  • Muhammad Raheel

    (Capital University of Science and Technology)

  • Samreen Ayaz

    (Capital University of Science and Technology)

  • Muhammad Tanvir Afzal

    (Capital University of Science and Technology)

Abstract

The scientific community has proposed diversified set of parameters to rank researchers, including publications, citations, h-index, different variants and extensions of h-index. However, there is a debate in the scientific Community which index ranks authors in a better way. Current state-of-the-art depicts that these indices are evaluated on imaginary case scenarios and small datasets. Furthermore, these indices are evaluated on different datasets, making it difficult to grasp the contribution and importance of each index over the others. To analyze the individual behavior of each index, these indices should comprehensively be evaluated on some extensive data set. This study emphasizes on the scrutiny of h-index, some of its variants and extensions to rank authors. These indices are evaluated using a comprehensive data set of Civil Engineering field. For the evaluation of results obtained from these indices, first correlation was calculated among indices. There exists weak correlation between various indices, which demonstrates that the author’s rankings acquired from these indices are not identical. Secondly, occurrences of awardees are checked in all ranked lists. The prestigious award winners of four Civil Engineering societies are considered as a benchmark. In top 10% of ranked list, maximum 47% of the awardees were brought by Wu-index. Overall, none of the index succeeded in bringing 100% awardees to the top rankings. Highest number of awardees on top of all ranked lists are found to be from ACI (American Concrete Institute), which shows ACI might be dependent on these indices for its criterion to honor awards.

Suggested Citation

  • Muhammad Raheel & Samreen Ayaz & Muhammad Tanvir Afzal, 2018. "Evaluation of h-index, its variants and extensions based on publication age & citation intensity in civil engineering," Scientometrics, Springer;Akadémiai Kiadó, vol. 114(3), pages 1107-1127, March.
    • Handle: RePEc:spr:scient:v:114:y:2018:i:3:d:10.1007_s11192-017-2633-2
    DOI: 10.1007/s11192-017-2633-2
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    References listed on IDEAS

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    Cited by:

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    2. Raminta Pranckutė, 2021. "Web of Science (WoS) and Scopus: The Titans of Bibliographic Information in Today’s Academic World," Publications, MDPI, vol. 9(1), pages 1-59, March.
    3. Rizwan Ghani & Faiza Qayyum & Muhammad Tanvir Afzal & Hermann Maurer, 2019. "Comprehensive evaluation of h-index and its extensions in the domain of mathematics," Scientometrics, Springer;Akadémiai Kiadó, vol. 118(3), pages 809-822, March.
    4. Madiha Ameer & Muhammad Tanvir Afzal, 2019. "Evaluation of h-index and its qualitative and quantitative variants in Neuroscience," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(2), pages 653-673, November.
    5. Qurat-ul Ain & Hira Riaz & Muhammad Tanvir Afzal, 2019. "Evaluation of h-index and its citation intensity based variants in the field of mathematics," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(1), pages 187-211, April.
    6. Muhammad Salman & Mohammad Masroor Ahmed & Muhammad Tanvir Afzal, 2021. "Assessment of author ranking indices based on multi-authorship," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(5), pages 4153-4172, May.
    7. Faiza Qayyum & Muhammad Tanvir Afzal, 2019. "Identification of important citations by exploiting research articles’ metadata and cue-terms from content," Scientometrics, Springer;Akadémiai Kiadó, vol. 118(1), pages 21-43, January.
    8. Mingyang Wang & Jiaqi Zhang & Shijia Jiao & Xiangrong Zhang & Na Zhu & Guangsheng Chen, 2020. "Important citation identification by exploiting the syntactic and contextual information of citations," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 2109-2129, December.
    9. Abdulrahman A. Alshdadi & Muhammad Usman & Madini O. Alassafi & Muhammad Tanvir Afzal & Rayed AlGhamdi, 2023. "Formulation of rules for the scientific community using deep learning," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(3), pages 1825-1852, March.
    10. Muhammad Usman & Ghulam Mustafa & Muhammad Tanvir Afzal, 2021. "Ranking of author assessment parameters using Logistic Regression," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(1), pages 335-353, January.

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