Co-authorship network of national researchers of Social Sciences in Mexico //Red de coautoría de investigadores nacionales en Ciencias Sociales en México

• Fernando Lámbarry-Vilchis Universidad del Zulia
• Juan Carlos Moreno-Jiménez Instituto Politécnico Nacional

DOI: https://doi.org/10.46925//rdluz.31.02

Resumen

Abstract

In Mexico, the most outstanding researchers are distinguished by the National Council of Science and Technology. Although, in the international literature, researcher's co-authorship networks and their impact on efficacy have been studied, in Mexico this type of studies is showing a greater boom, so the objective of this paper is to analyze the structure of the network of co-authorships of the researchers in Social Sciences level 3 of the country. For this purpose, the research method was based on the theory of networks and specifically on topology metrics. One of the conclusions of the research is that the researchers under study publish in a similar proportion individually or collaboratively, configuring a fragmented co-authorship network with a main component with properties that are explained by both the small-world and the free-scale model.

Resumen

En México los investigadores más destacados son distinguidos por el Consejo Nacional de Ciencia y Tecnología. Aunque en la literatura internacional se han estudiado las redes de coautoría de los investigadores y su impacto en la eficacia, en México este tipo de estudios están presentando mayor auge, por lo que el objetivo de este trabajo es analizar la estructura de la red de coautorías de los investigadores en Ciencias Sociales nivel 3 del país. Para ello, el método de investigación se fundamentó en la teoría de redes y específicamente en las métricas de topología. Una de las conclusiones de la investigación es que los investigadores bajo estudio, publican en similar proporción en forma individual o colaborativamente, configurando una red de coautoría fragmentada con un componente principal con propiedades que se explican tanto por el modelo de mundo pequeño, como de libre escala.

Citas

Acedo, F. J., Barroso, C., Casanueva, C., and Galán, J. L. (2006). Co‐authorship in management and organizational studies: An empirical and network analysis. Journal of Management Studies, 43(5), 957-983. DOI: doi.org/10.1111/j.1467-6486.2006.00625.x

Albert, R., and Barabási, A. L. (2002). Statistical mechanics of complex networks. Reviews of modern physics, 74(1), 47. DOI: 10.1103/RevModPhys.74.47

Badar, K., Frantz, T.L. and Jabeen, M. (2016). Research performance and degree centrality in co-authorship networks. Aslib Journal of Information Management, 68 (6), 756 – 771, DOI: 10.1108/AJIM-07-2016-0103

Badar, K., Hite, J.M. and Badir, Y.F. (2014). The moderating roles of academic age and institutional sector on the relationship between co-authorship network centrality and academic research performance. Aslib Journal of Information Management, 66 (1), 38 – 53. DOI: 10.1108/AJIM-05-2013-0040

Barabási A.L., and Albert R. (1999). Emergence of Scaling in Random Networks, Science 286 (5439), 509-512; DOI: 10.1126/science.286.5439.509.

Barabási, A. L., and Albert, R. (2002). Statistical mechanics of complex networks, Reviews of Modern Physics, 74 (1), 47. DOI: 10.1103/RevModPhys.74.47

Barabási, A. L., Jeong, H., Néda, Z., Ravasz, E., Schubert, A., y Vicsek, T. (2002b). Evolution of the social network of scientific collaborations. Physica A: Statistical mechanics and its applications, 311(3), 590-614. DOI: 10.1016/S0378-4371(02)00736-7

Bordons, M., Aparicio, J., González-Albo, B. and Díaz-Faes, A. (2015). The relationship between the research performance of scientists and their position in co-authorship networks in three fields, Journal of Informetrics, 9 (1), 135-144. DOI:10.1016/j.joi.2014.12.001

Borgatti, S.P., (2005). Centrality and network flow. Social Networks, 27 (1), 55–71. doi:10.1016/j.socnet.2004.11.008.

Cheng, M. Y., Hen, K. W., Tan, H. P., and Fok, K. F. (2013). Patterns of co-authorship and research collaboration in Malaysia. Aslib Proceedings, 65(6), 659–674. DOI: 10.1108/AP-12-2012-0094

Consejo Nacional de Ciencia y Tecnología (Conacyt) (2014). Tecnología e Innovación 2014-2018. CONACYT (Vol. 409778). DOF.

De Stefano, D., Fuccella, V., Prosperina, M. and Zaccarin, S. (2013). The use of different data sources in the analysis of co-authorship networks and scientific performance. Social Networks, 35, 370– 381. DOI: 10.1016/j.socnet.2013.04.004

Freeman, L.C. (1979). Centrality in social networks conceptual clarification. Social Networks, 1(3), 215-239, DOI: 10.1016/0378-8733(78)90021-7.

Gobea, S. (2005). El Modelo Matemático de Lotka: su aplicación a la producción científica latinoamericana en ciencias bibliotecológica y de la información. México: UNAM Centro Universitario de Investigaciones Bibliotecológicas.

Gonzalez-Brambila, C.N. (2014). Social capital in academia. Scientometrics, 101 (3), 1609-1625. DOI: 10.1007/s11192-014-1424-2.

Goyal, S., Van der Leij, M.J. and Moraga-Gonzalez, J.L. (2006). Economics: an emerging small world. Journal of Political Economy, 114, 403–412. https://hdl.handle.net/11245/1.400360, DOI: 10.1086/500990

Kretschmer, H. (2004), “Author productivity and geodesic distance in bibliographic co-authorship networks, and visibility on the web”, Scientometrics, Vol. 60, No. 3, pp. 409-420.

Kronegger, L., Ferligoj, A., and Doreian, P. (2011). On the dynamics of national scientific systems. Quality & Quantity 45, 989–1015. DOI: 10.1007/s11135-011-9484-3.

Kumar, S., and Mohd, J. (2014). "Relationship between authors’ structural position in the collaboration network and research productivity: Case of Indian earth scientists", Program, Vol. 48 Issue: 4, pp.355-369, https://doi.org/10.1108/PROG-01-2013-0002

Laband, D. N., and Tollison, R. D. (2000). Intellectual collaboration. Journal of Political economy, 108(3), 632-662. DOI: 10.1086/262132

Lee, S., and Bozeman, B. (2005). The impact of research collaboration on scientific productivity. Social Studies of Science 35, 673–702. http://www.jstor.org/stable/25046667, DOI:10.1177/0306312705052359

Li, E.Y., Liao, C.H. and Yen, R. (2013). Co-authorship networks and research impact: a social capital perspective. Research Policy, 42 (9), 1515-1530. DOI: 10.1016/j.respol.2013.06.012

Liberman, S., and Wolf, K.B. (2010). Bonding number in scientific disciplines. Social Networks 20, 239–246. DOI: 10.1016/S0378-8733(98)00003-3.

Lopaciuk-Gonczaryk, B. (2016). Collaboration strategies for publishing articles in internationaljournals – A study of Polish scientists in economics. Social Networks, 44 , 50-63. DOI 10.1016/j.socnet.2015.07.001

Lotka, A. (1926). The Frequency distribution of scientific productivity. Journal of the Washington Academy of Sciences 16 (12): 317-323, 1926.

Lu, H., and Feng, Y., (2009). A measure of authors’ centrality in co-authorship networksbased on the distribution of collaborative relationships. Scientometrics, 81 (2), 499–511. ISSN: 0138-9130 (Print) 1588-2861 (Online)

Milgram, S. (1967). The Small-World Problem. Psychology Today, 1 (1), May 1967, 61‐67.

Moody, J. (2004). The structure of a social science collaboration network: disciplinary cohesion from 1963 to 1999. American Sociological Review, 69 (2), 213–238. DOI: 10.1177/000312240406900204

Newman, M. (2004a). Coauthorship networks and patterns of scientific collaboration. Proceedings of the National Academy of Sciences. 101 (1), 5200-5205. http://www.pnas.org/content/101/suppl_1/5200.full, DOI: 10.1073/pnas.0307545100

Newman, M. (2004b). Who is the best connected scientist? A study of scientific coauthorship networks. Complex Networks, 650, 337-370. DOI: 10.1103/PhysRevE.64.016132

Newman, M.E. (2001). The structure of scientific collaboration networks. Proceedings of the National Academy of Sciences, 98 (2), 404-409. DOI: 10.1073/pnas.98.2.404

Nieminen, J. (1974). On centrality in a graph. Scandinavian Journal of Psychology 15:322-336. DOI: 10.1111/j.1467-9450.1974.tb00598.x

Otte, E., and Rousseau, R. (2002), “Social network analysis: a powerful strategy, also for the information sciences”, Journal of Information Science, Vol. 28, No. 6, pp. 441-453.

Ponomariov, B. L., and Boardman, P. C. (2010). Influencing scientists’ collaboration and productivity patterns through new institutions: University research centers and scientific and technical human capital. Research Policy, 39(5), 613-624. DOI: https://doi.org/10.1016/j.respol.2018.03.008

Rossoni L. (2014). Agency and small worlds networks: a multilevel analysis of academic productivity. RAM. Revista de Administração Mackenzie, 15(1), 200-235. DOI: 10.1590/S1678-69712014000100009

Tang, L., and Walsh, J.P. (2010), “Bibliometric fingerprints: name disambiguation based on approximate structure equivalence of cognitive maps”, Scientometrics, Vol. 84, No. 3, pp. 763-784.

Watts, D., and Strogatz, S., (1998). The small world problema. Collective dynamics of small world networks. Nature 393, 440–442. DOI: 10.1038/30918

Wuchty, S., Jones, B.F., and Uzzi, B. (2007). The Increasing Dominance of Teams in Production of Knowledge. Science, 316 (5827), 1036-1039.

DOI: 10.1126/science.1136099

Ye, Q., Li, T., and Law, R. (2013). A co-authorship network analysis of tourism and hospitality research collaboration. Journal of Hospitality & Tourism Research, 37, (1), 51-76. DOI: 10.1177/1096348011425500