## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Publication Number**} & \multirow{2}{*}{\(n\)} & \multirow{2}{*}{\(j\)} & \multicolumn{2}{c}{**Researcher A**} & \multicolumn{4}{c}{**Researcher B**} \\ \cline{2-13}  & & & & **Citations** & **IF** & **Publication** & \(n\) & \(j\) & **Citations** & **IF** & **Publication** \\  & & & **Received** & **(Journal)** & **Date** & **Received** & **(Journal)** & **Date** \\ \hline
1 & 4 & 1 & 31 & 3.6 & 01-MAR-2017 & 2 & 1 & 9 & 2.2 & 01-OCT-2019 \\
2 & 2 & 2 & 55 & 10.23 & 15-MAX-2019 & 4 & 2 & 47 & 9.4 & 30-NOV-2019 \\
3 & 7 & 7 & 17 & 4.2 & 15-DEC-2019 & 9 & 9 & 11 & 2.2 & 01-MAR-2020 \\
4 & 1 & 1 & 10 & 1.3 & 15-FEB-2020 & 3 & 2 & 4 & 1.9 & 15-DEC-2020 \\
5 & 12 & 7 & 15 & 3.6 & 01-OCT-2020 & 12 & 3 & 3 & 5.3 & 15-APR-2021 \\
6 & 3 & 2 & 4 & 2.5 & 01-FEB-2021 & 9 & 6 & 0 & 9.4 & 01-JU-2021 \\
7 & 6 & 1 & 1 & 4.2 & 15-MAX-2021 & - & - & - & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Assumed data regarding the publications of two imaginary researchers. The current date is taken as 01-NOV-2021. The rating of a researcher is the sum of their \(R\)Cs displayed in the last column of Table 6. The ratings of researchers A and B are thus 22.73 and 13.76, respectively. Publications number 2 for both researchers have yielded the largest contributions because of the large number of citations they have received and an early position of the researchers in the sequence of the authors lists. An increase in the value of \(k\) would cause a decrease in the rating of researcher A and an increase in that of B. For instance, setting \(k\) = 0.6 yields ratings of 21.3 and 14.29 in respect of researchers A and B, respectively. The reason behind this observation is that researcher A appears in the authors lists of the high citation/IF publications much closer to the ends of the sequences than does researcher B. Thus, a high value of \(k\) favors researcher B, whereas researcher A would be happier with a low value. The superiority of the proposed method can be judged from the credit evaluations of publications number 3 and 5 of researcher A and 3, 5, and 6 of B. Had the measure of quantifying a researcher's credit based on the number of coauthors and the researcher's position not been incorporated, all the coauthors would have individually taken the whole credit of the publication. In this case, every coauthor of the articles 3(A), 5(A), 3(B), 5(B), and 6(B) would have secured 9.03, 13.83, 6.58, 10.6, and 18.8 credits, respectively. Furthermore, an older publication would have stood taller based merely on the cumulative number of citations received throughout the years after its publication. The researcher rating approach presented in this article justly addresses the issue of adjusting the number of coauthors, the sequence number of the researcher in the authors list, and the time elapsed after publication of the article in assigning the due credit. A limitation to the proposed method lies in the true reflection of the actual contributions made by the coauthors of a multi-authored publication. Pragmatically speaking, the contribution share is rarely distributed in a uniformly decreasing pattern through the sequence of the coauthors as is modeled in this work. Obviously, the true picture of individual contributions can only be visualized if the publication source has provided such information. Some journals do provide such information but mostly in a very qualitative way. The individual contributions are normally divided into various contribution fields, such as conceptualization, investigation, validation, funding, write-up, and more. It becomes very difficult to quantify the individual contributions of the coauthors in percentages from these qualitative fields. Even if some researchers have tried to model the actual contributions to get the shares in numbers, the methods have not gained much popularity.