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Ranking of author assessment parameters using Logistic Regression

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Abstract

The renowned international scientific societies nominate researchers for awards based on qualitative judgments every year. Qualitative judgment uses subjective assessments based on information that is not quantifiable. The way of assessing the quality of the work has not been established or disclosed, nor do we have any qualitative evaluation criteria. We can assess the quality of the researcher's work by mapping the quantitative parameters to qualitative judgments. To date, the scientific community has presented more than 50 research assessment quantitative parameters, including publication count, citation count, h-index, and its variants. The contemporary state-of-the-art in authors ranking does not determine the best parameter that effectively maps on experts' qualitative evaluation. Moreover, these parameters have been evaluated by using same scenarios. In such scenarios, the value and effect of each parameter over the others are complicated to ascertain. Therefore, they must be assessed in inequitable scenarios. The purpose of this research is to identify the significant parameters that map on qualitative judgments of international scientific societies in Civil Engineering (CE) for award nominations. We will identify the rank of author assessment parameters, which includes published papers, citations, No of years since 1st publication, citations in h-core, authors/paper, citations/paper, citations/year, h-index, g-index, hg-index, A-index, R-index, e-index, and f-index. We have evaluated these parameters on the dataset from the discipline of Civil Engineering (CE). The data set contains 250 non-award winners and 250 award winners from prestigious scientific societies of CE. The h-index and its variants have been ranked based on their effectiveness for awardees using Logistic Regression. The award-winning researchers have less number of average authors/paper than the non-awardees. The authors/paper has achieved the highest effectiveness of 67% for awardees. Furthermore, we have also analyzed the ratio of awardees in the ranked list of 50, 100, and 150 researchers by author assessment parameters. The authors/papers have outperformed all other indices by elevating 62% and 66% of the award recipients in its ranked list of 100 and 150 researchers. In the ranked list of 50 researchers, publications elevate 54% awardees, and Authors/papers achieved the second-highest elevation score of awardees of 50%.

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Notes

  1. https://www.icevirtuallibrary.com/page/authors.

  2. https://www.concrete.org/aboutaci/honorsandawards/awards/.

  3. https://csce.ca/en/committees/honours-and-fellowships/.

  4. https://www.asce.org/awards/.

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Authors and Affiliations

  1. FAST- National University of Computer & Emerging Sciences, Islamabad Campus, Islamabad, Pakistan

    Muhammad Usman

  2. Capital University of Science and Technology, Islamabad, Pakistan

    Ghulam Mustafa

  3. Namal Institute Mianwali, Mianwali, Pakistan

    Muhammad Tanvir Afzal

Authors
  1. Muhammad Usman
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  2. Ghulam Mustafa
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  3. Muhammad Tanvir Afzal
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Correspondence to Muhammad Tanvir Afzal.

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Usman, M., Mustafa, G. & Afzal, M.T. Ranking of author assessment parameters using Logistic Regression. Scientometrics 126, 335–353 (2021). https://doi.org/10.1007/s11192-020-03769-y

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