Abstract
The problem on how to distribute the publication credits among ordered coauthors has been extensively discussed in the literature. However, there is no consensus about what is the most adequate procedure. This paper studies the properties of the existing counting methods and shows an impossibility result regarding the existence of a general counting method able to satisfy no advantageous merging and no advantageous splitting simultaneously—two properties that we consider fundamental. Our results suggest that the generalized variations of the geometric and the harmonic counting methods are the most flexible and robust in theoretical terms.
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Notes
This approach is valid for \(n\geqslant 4\). The case \(n=3\) is obtained by extrapolation, and in the case \(n=2\) both coauthors are equally treated. If the first and the corresponding authors are the same, the formula should be adjusted accordingly.
There is some relation between some of the axioms in Stallings et al. (2013) and some of the properties that we will discuss below. Axiom 1 is implied by properties NNC and OrM. Axiom 2 demands normalization, which is a working assumption that we impose from the beginning.
References
Abbas, A. M. (2010). Generalized linear weights for sharing credits among multiple authors. arXiv preprint arXiv:1012.5477.
Abbas, A. M. (2011). Polynomial weights or generalized geometric weights: Yet another scheme for assigning credits to multiple authors. arXiv preprint arXiv:1103.2848.
Abramo, G., D'Angelo, C. A., & Rosati, F. (2013). The importance of accounting for the number of co-authors and their order when assessing research performance at the individual level in the life sciences. Journal of Informetrics, 7(1), 198–208.
Assimakis, N., & Adam, M. (2010). A new authors productivity index: P-index. Scientometrics, 85(2), 415–427.
Bornmann, L., Mutz, R., Hug, S. E., & Daniel, H.-D. (2011). A multilevel meta-analysis of studies reporting correlations between the h index and 37 different h index variants. Journal of Informetrics, 5(3), 346–359.
Caruso, E. M., Epley, N., & Bazerman, M. H. (2006). The costs and benefits of undoing egocentric responsibility assessments in groups. Journal of Personality and Social Psychology, 91(5), 857–871.
Cole, J. R., & Cole, S. (1974). Social stratification in science. American Journal of Physics, 42(10), 923–924.
Cronin, B. (2001). Hyperauthorship: A postmodern perversion or evidence of a structural shift in scholarly communication practices? Journal of the Association for Information Science and Technology, 52(7), 558–569.
Egghe, L., Rousseau, R., & Van Hooydonk, G. (2000). Methods for accrediting publications to authors or countries: Consequences for evaluation studies. Journal of the Association for Information Science and Technology, 51(2), 145–157.
Gazni, A., Sugimoto, C. R., & Didegah, F. (2012). Mapping world scientific collaboration: Authors, institutions, and countries. Journal of the Association for Information Science and Technology, 63(2), 323–335.
Hagen, N. T. (2008). Harmonic allocation of authorship credit: Source-level correction of bibliometric bias assures accurate publication and citation analysis. PLoS ONE, 3(12), e4021.
Hagen, N. T. (2010). Harmonic publication and citation counting: Sharing authorship credit equitably-not equally, geometrically or arithmetically. Scientometrics, 84(3), 785–793.
Hagen, N. T. (2013). Harmonic coauthor credit: A parsimonious quantification of the byline hierarchy. Journal of Informetrics, 7(4), 784–791.
Hu, X. (2009). Loads of special authorship functions: Linear growth in the percentage of equal first authors and corresponding authors. Journal of the Association for Information Science and Technology, 60(11), 2378–2381.
Katz, J. S., & Martin, B. R. (1997). What is research collaboration? Research Policy, 26(1), 1–18.
Kim, J., & Diesner, J. (2014). A network-based approach to coauthorship credit allocation. Scientometrics, 101(1), 587–602.
Kim, J., & Kim, J. (2015). Rethinking the comparison of coauthorship credit allocation schemes. Journal of Informetrics, 9(3), 667–673.
Larivière, V., Gingras, Y., Sugimoto, C. R., & Tsou, A. (2015). Team size matters: Collaboration and scientific impact since 1900. Journal of the Association for Information Science and Technology, 66(7), 1323–1332.
Lindsey, D. (1980). Production and citation measures in the sociology of science: The problem of multiple authorship. Social Studies of Science, 10(2), 145–162.
Liu, X. Z., & Fang, H. (2012). Fairly sharing the credit of multi-authored papers and its application in the modification of h-index and g-index. Scientometrics, 1(91), 37–49.
Lukovits, I., & Vinkler, P. (1995). Correct credit distribution: A model for sharing credit among coauthors. Social Indicators Research, 36(1), 91–98.
Maciejovsky, B., Budescu, D. V., & Ariely, D. (2009). Research notethe researcher as a consumer of scientific publications: How do name-ordering conventions affect inferences about contribution credits? Marketing Science, 28(3), 589–598.
O’Neill, B. (1982). A problem of rights arbitration from the talmud. Mathematical Social Sciences, 2(4), 345–371.
Persson, O., Glänzel, W., & Danell, R. (2004). Inflationary bibliometric values: The role of scientific collaboration and the need for relative indicators in evaluative studies. Scientometrics, 60(3), 421–432.
Price, D. D. S. (1981). Multiple authorship. Science, 212(4498), 986–986.
Sekercioglu, C. H. (2008). Quantifying coauthor contributions. Science, 322(5900), 371.
Stallings, J., Vance, E., Yang, J., Vannier, M. W., Liang, J., Pang, L., et al. (2013). Determining scientific impact using a collaboration index. Proceedings of the National Academy of Sciences, 110(24), 9680–9685.
Trenchard, P. M. (1992). Hierarchical bibliometry: A new objective measure of individual scientific performance to replace publication counts and to complement citation measures. Journal of Information Science, 18(1), 69–75.
Trueba, F. J., & Guerrero, H. (2004). A robust formula to credit authors for their publications. Scientometrics, 60(2), 181–204.
Tscharntke, T., Hochberg, M. E., Rand, T. A., Resh, V. H., & Krauss, J. (2007). Author sequence and credit for contributions in multiauthored publications. PLoS Biology, 5(1), e18.
Van Hooydonk, G. (1997). Fractional counting of multiauthored publications: Consequences for the impact of authors. Journal of the American Society for Information Science, 48(10), 944–945.
Vinkler, P. (2000). Evaluation of the publication activity of research teams by means of scientometric indicators. Current Science, 79, 602–612.
Waltman, L. (2016). A review of the literature on citation impact indicators. Journal of Informetrics, 10(2), 365–391.
Wren, J. D., Kozak, K. Z., Johnson, K. R., Deakyne, S. J., Schilling, L. M., & Dellavalle, R. P. (2007). The write position: A survey of perceived contributions to papers based on byline position and number of authors. EMBO Reports, 8(11), 988–991.
Wuchty, S., Jones, B. F., & Uzzi, B. (2007). The increasing dominance of teams in production of knowledge. Science, 316(5827), 1036–1039.
Xu, J., Ding, Y., Song, M., & Chambers, T. (2016). Author credit-assignment schemas: A comparison and analysis. Journal of the Association for Information Science and Technology, 67(8), 1973–1989.
Zhang, C.-T. (2009). A proposal for calculating weighted citations based on author rank. EMBO Reports, 10(5), 416–417.
Acknowledgements
Author wish to thank to Juan Pablo Rincón-Zapatero and Ludo Waltman, as well as several seminar and congress participants for helpful comments and discussions. Financial support from the Spanish Ministerio of Ciencia y Innovación project ECO2016-75410-P, GRODE and the Barcelona GSE is gratefully acknowledged. The usual caveat applies.
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Appendix
Appendix
Proof of Proposition 1
Following the discussion preceding Proposition 1, start by considering NAS (i.e., the lower bound). In this case, for \(n=1\) we would have \(c_{1}^{2}+c_{2}^{2}\leqslant c_{1}^{1},\) which is feasible because \(c_{1}^{n}+\cdots +c_{n}^{n}=1,\) and we have \(c_{1}^{2}\geqslant c_{2}^{2}\geqslant 0\). For \(n=2,\) we would have \(c_{2}^{3}+c_{3}^{3}\leqslant c_{2}^{2}\) and \(c_{1}^{3}+c_{3}^{3}\leqslant c_{1}^{2},\) that would imply \(c_{1}^{3}+c_{3}^{3}+c_{2}^{3}+c_{3}^{3}\leqslant c_{1}^{1}=1\) (by \(c_{1}^{n}+\cdots +c_{n}^{n}=1\)). This inequality is satisfied if \(c_{3}^{3}=0.\) In this case, in order to satisfy OrM, we can have \(c_{1}^{3}\geqslant c_{2}^{3}\geqslant c_{3}^{3}=0,\) but to satisfy CoM, we must also have \(c_{2}^{3}=c_{2}^{2}\) and \(c_{1}^{3}=c_{1}^{2}.\) For \(n=3,\) we would have \(c_{3}^{4}+c_{4}^{4}\leqslant c_{3}^{3}, c_{2}^{4}+c_{4}^{4}\leqslant c_{2}^{3}\) and \(c_{1}^{4}+c_{4}^{4}\leqslant c_{1}^{3},\) that would imply \(c_{1}^{4}+c_{4}^{4}+c_{2}^{4}+c_{4}^{4}+c_{3}^{4}+c_{4}^{4}\leqslant c_{1}^{1}=1\) (by \(c_{1}^{n}+\cdots +c_{n}^{n}=1\)). This inequality is satisfied if \(c_{4}^{4}=0.\) In this case, in order to satisfy OrM, we can have \(c_{1}^{4}\geqslant c_{2}^{4}\geqslant c_{3}^{4}\geqslant c_{4}^{4}=0,\) but to satisfy CoM, we must also have \(c_{3}^{4}=c_{3}^{3}=0, c_{2}^{4}=c_{2}^{3}\) and \(c_{1}^{4}=c_{1}^{3}.\) This construction can be generalized for every n, to obtain that any counting method satisfying NAS (i.e., the lower bound) is required to satisfy \(c_{1}^{n}\geqslant c_{2}^{n}\geqslant 0\) and \(c_{i}^{n}=0\) for \(i>2\) with \(c_{1}^{n}=c_{1}\) and \(c_{2}^{n}=c_{2}\) constant for all \(n\geqslant 2.\)
Now, consider NAM (i.e., the upper bound). In this case, \(n=1\) is not defined. For \(n=2,\) we would have \(c_{2}^{2}\leqslant c_{2}^{3}+c_{j}^{3}\) and \(c_{1}^{2}\leqslant c_{1}^{3}+c_{j}^{3},\) where \(j=2,3\) denote the possible values that \(j>i\) can take. However, by OrM, the case that is most difficult to satisfy NAM occurs when \(c_{j}^{3}\) takes the smallest value, i.e., at \(j=3.\) In this case, the sum of the previous inequalities would imply \(c_{1}^{1}=1\leqslant c_{1}^{3}+c_{3}^{3}+c_{2}^{3}+c_{3}^{3}\) (by \(c_{1}^{n}+\cdots+c_{n}^{n}=1\)). This inequality is always satisfied. The consideration of each of these two inequalities under the previously found NAS condition \(c_{3}^{3}=0\) implies that \(c_{2}^{2}\leqslant c_{2}^{3}\) and \(c_{1}^{2}\leqslant c_{1}^{3},\) which by CoM require that \(c_{2}^{2}=c_{2}^{3}\) and \(c_{1}^{2}=c_{1}^{3}.\) Similarly, for \(n=3,\) the restriction is imposed by each inequality \(c_{3}^{3}\leqslant c_{3}^{4}+c_{4}^{4}, c_{2}^{3}\leqslant c_{2}^{4}+c_{4}^{4}\) and \(c_{1}^{3}\leqslant c_{1}^{4}+c_{4}^{4},\) which must be satisfied with equality if the previously found NAS condition \(c_{4}^{4}=0\) and CoM are satisfied. This argument can be generalized for every n, to obtain that any counting method satisfying NAM does not add further restrictions other than NAS. \(\square\)
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Osório, A. On the impossibility of a perfect counting method to allocate the credits of multi-authored publications. Scientometrics 116, 2161–2173 (2018). https://doi.org/10.1007/s11192-018-2815-6
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DOI: https://doi.org/10.1007/s11192-018-2815-6