Choosing a journal to publish a work is a task that involves many variables. Usually, the authors’ experience allows them to classify journals into categories, according to their suitability and the characteristics of the article. However, there are certain aspects in the choice that are probabilistic in nature, whose modelling may provide some help. Suppose an author has to choose a journal from a preference list to publish an article. The researcher is interested in publishing the paper in a journal with a rank number less than or equal to k. For this purpose, a simple classification model is presented in order to choose the best journal from the list, from which some fundamental consequences can be deduced and simple rules derived. For example, if the list contains 100 journals and is ordered using 2-year impact factor, the rule “send to the journal at the k − 10 position” is adequate.
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Althouse, B.M., West, J.D., Bergstrom, C.T., Bergstrom, T. (2009). Differences in impact factor across fields and over time. Journal of the Association for Information Science and Technology, 60(1), 27–34.
Black, S. (2012). How much do core journals change over a decade? Library Resources and Technical Services, 56, 80–93.
Bradshaw, C.J., & Brook, B.W. (2016). How to rank journals. PloS One, 11 (3), e0149852.
Ferrer-Sapena, A., Diaz-Novillo, S., Sánchez-Pérez, E.A. (2017). Measuring time-dynamics and time-stability of Journal Rankings in Mathematics and Physics by Means of Fractional p-Variations. Publications, 5(3), 21.
Ferrer-Sapena, A., Sánchez-Pérez, E.A., González, L.M., Peset, F., Aleixandre-Benavent, R. (2015). Mathematical properties of weighted impact factors based on measures of prestige of the citing journals. Scientometrics, 105(3), 2089–2108.
Ferrer-Sapena, A., Sánchez-Pérez, E.A., González, L.M., Peset, F., Aleixandre-Benavent, R. (2016). The impact factor as a measuring tool of the prestige of the journals in research assessment in mathematics. Research Evaluation, 25(3), 306–314.
Gastel, B., & Day, R.A. (2016). How to write and publish a scientific paper. Santa Barbara: ABC-CLIO.
Gibbs, A. (2016). Improving publication: advice for busy higher education academics. International Journal for Academic Development, 21(3), 255–258.
Grabisch, M.M., Marichal, J.L., Mesiar, R., Pap, E. (2009). Aggregation functions. Cambridge: Cambridge University Press.
Haghdoost, A., Zare, M., Bazrafshan, A. (2014). How variable are the journal impact measures?. Online Information Review, 38, 723–737.
Hol, E.M. (2013). Empirical studies on volatility in international stock markets. Berlin: Springer.
Kelly, J.L. Jr. (2011). A new interpretation of information rate. Manuscript. Reprinted in: The Kelly capital growth investment criterion: theory and practice (pp. 25–34).
Klement, E.P., Mesiar, R., Pap, E. (2000). Triangular norms. Dordrecht/Boston/London: Kluwer Academic Publishers.
Klement, E.P., Mesiar, R., Pap, E. (2001). Uniform approximation of associative copulas by strict and non-strict copulas. Illinois Journal of Mathematics, 45 (4), 1393–1400.
Lawrence, P.A. (2003). The politics of publication. Nature, 422(6929), 259–261.
Lu, J., Wu, D., Mao, M., Wang, W., Zhang, G. (2015). Recommender system application developments: a survey. Decision Support Systems, 74, 12–32.
Mansilla, R., Köppen, E., Cocho, G., Miramontes, P. (2007). On the behavior of journal impact factor rank-order distribution. Journal of Informetrics, 1, 155–160.
Murtagh, F., Orlov, M., Mirkin, B. (2018). Qualitative judgement of research impact: Domain taxonomy as a fundamental framework for judgement of the quality of research. Journal of Classification, 35, 5–28.
Neff, B.D., & Olden, J.D. (2010). Not so fast: inflation in impact factors contributes to apparent improvements in journal quality. BioScience, 60(6), 455–459.
Nelsen, R.B. (1999). An introduction to copulas. New York: Springer.
Pajić, D. (2015). On the stability of citation-based journal rankings. Journal of Informetric, 9, 990–1006.
Shannon, C.E. (1948). A mathematical theory of communication. The Bell System Technical Journal, 27, 379–423. Reprinted in: ACM SIGMOBILE Mobile Computing and Communications Review, 5(1)(2001), 3-55.
Xu, H., Martin, E., Mahidadia, A. (2014). Contents and time sensitive document ranking of scientific literature. Journal of Informetrics, 8, 546–561.
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In this Appendix, we give the algorithm that the reader can use to calculate by himself the parameters and plots of our model. We will explicitly give the commands to be used in Mathematica Ⓒ 2019 Wolfram, but any other mathematical software can be used.
For the calculus of Fig. 1, first of all, we need to define the table with data:
After that, we proceed to fit the data to a parabolic shape, that is, to find the “best” parabola in terms of minimizing the quadratic errors. This can be done using the Mathematica command Fit
The first argument corresponds to the name we have given to the data table, the second argument is given by the elements of the parabola and the third is the variable of the fitting, in this case x. After that, we are able to plot both, the data and our parabolic approximation. For doing that, we type:
and for plotting the fitted polynomial
that produces the plot corresponding to Fig. 1,
Then, the second element to be calculated in our model is the parameter b. For example, in the case of Table 2, this can be introduced in Mathematica by the command Table
and the command for plotting:
that produces Fig. 3
Finally, the probability can be easily estimated by the definition of a function of several variables, whose meaning is described in the text. Then,
If we plot this probability for the values k = 25, Nmax= 100, a = 0.235878, we obtain the plot corresponding to Fig. 4 (right).
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Ferrer-Sapena, A., Calabuig, J.M., García Raffi, L.M. et al. Where Should I Submit My Work for Publication? An Asymmetrical Classification Model to Optimize Choice. J Classif 37, 490–508 (2020). https://doi.org/10.1007/s00357-019-09331-7