Abstract
In this chapter we model the multilevel structure of scientific work, looking at social networks of collaborations between scientists, and at how these networks are embedded in disciplinary and organizational levels. Once the relational structure of scientific collaboration is described, we look at the role that it plays in scholars’ successes. We adopt the linked-design approach to analyse the local system of public funding to academic disciplines in Italy using bipartite networks across disciplinary areas. We thus analyse the mechanisms that lie beyond the structure of research project collaborations in Italian academia. We find that individual attributes (being a national coordinator, a full professor, and having being promoted) play a role in getting funded. It is however the position of being a broker across otherwise unconnected research groups that makes a difference in the total amount of funding received by a scientist over the years under analysis, in some cases combined with egonet closure. These results confirm the importance of looking at individual network properties when analyzing scientific collaborations. Leadership is a characteristic that seems to be related both to career achievements (becoming a full professor) and to the capability of attracting multiple research groups for scientific collaborations.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Anselin, L., & Hudak, S. (1992). Spatial econometrics in practice: A review of software options. Regional Science and Urban Economics, Elsevier, 22(3), 509–536.
Bellotti, E. (2012). Getting funded. Multi-level network of physicists in Italy. Social Networks, 34, 215–229.
Bellotti, E. (2014). Qualitative networks. Mixed methods in sociological research. London: Routledge.
Beltrame, L. (2008). La struttura del campo scientifico: una geografia delle traiettorie dei fisici delle particelle. In E. Bellotti, L. Beltrame, & P. Volontè (Eds.), Il campo sociale della fisica particellare in Italia. uno studio sociologico. Bolzano: Bolzano University Press.
Borgatti, S. P., Everett, M. G., & Freeman, L. C. (2002). Ucinet 6 for Windows: Software for social network analysis. Harvard: Analytic Technologies.
Breiger, R. (1974). The duality of persons and groups. Social Forces, 53, 181–190.
Browne, W. J., Goldstein, H., & Rasbash, J. (2001). Multiple membership multiple classification (MMMC) models. Statistical Modelling, 1, 103–124.
Burt, R. S. (2005). Brokerage and closure. An introduction to social capital. Oxford: Oxford University Press.
Butts, C. (2007). sna: Tools for social network analysis. Statnet Project http://statnetproject.org/, Seattle, WA. R package version 1.5, http://cran.r-project.org/package=sna
Cliff, A. D., & Ord, J. K. (1975). Spatial autocorrelation. London: Pion.
Daraganova, G., & Robins, G. (2013). Autologistic actor attribute models. In D. Lusher, J. Koskinen, & G. Robins (Eds.), Exponential random graph models for social networks. New York: Cambridge University Press.
Doreian, P. D. (1992). Models of network effects on social actors. In L. C. Freeman, D. R. White, & A. K. Kimball Romney (Eds.), Research methods in social network analysis. New Brunswick: Transaction Pub.
Dow, M. M., White, D. R., & Burton, M. L. (1983). Multivariate modeling with interdependent network data. Behavior Science Research, 17, 216–245.
Dow, M. M., Burton, M. L., White, D. R., & Reitz, K. P. (1984). Galton’s problem as network autocorrelation. American Ethnologist, 11, 754–770.
Erosheva, E. A., & Fienberg, S. E. (2011). Mixed membership models. In M. Lovric (Ed.), International encyclopedia of statistical science (pp. 824–826). Berlin: Springer.
Fararo, T. J., & Doreian, P. (1984). Tripartite structural analysis: Generalizing the Breiger–Wilson formalism. Social Networks, 6, 141–175.
Freeman, L. C. (1979). Centrality in social networks: Conceptual clarification. Social Networks, 1(3), 215–239.
Freeman, L. C., Borgatti, S. P., & White, D. R. (1991). Centrality in valued graphs: A measure of betweenness based on network flow. Social Networks, 13(2), 141–154.
Lazega, E., Jourda, M. T., Mounier, L., & Stofer, R. (2008). Catching up with big fish in the big pond? Multi-level network analysis through linked design. Social Networks, 30, 157–176.
Leenders, R. T. A. J. (2002). Modeling social influence through network autocorrelation: Constructing the weight matrix. Social Networks, 24, 21–47.
Naroll, R. (1961). Two solutions to Galton’s problem. Philosophy of Science, 28, 15–39.
Naroll, R. (1965). Galton’s problem: The logic of cross-cultural analysis. Social Research, 32, 428–451.
Ord, K. (1975). Estimation methods for models of spatial interaction. Journal of the American Statistical Association, 70, 120–126.
Schaefer, J. (Ed.). (1974). Studies in cultural diffusion: Galton’s problem. New Haven: HRAFlex Books.
Snijders, T. A. B., & Bosker, R. J. (2012). Multilevel analysis: An introduction to basic and advanced multilevel modelling (2nd ed.). London: Sage.
Steglich, C. E. G., Snijders, T. A. B., & Pearson, M. (2010). Dynamic networks and behavior: Separating selection from influence. Sociological Methodology, 40(1), 329–393.
Tranmer, M., Steel, D., & Browne, W. J. (2014). Multiple-membership multiple-classification models for social network and group dependences. Journal of the Royal Statistical Society: Series A (Statistics in Society), 177(Part 2), 439–455.
Wang, P., Robins, G., Pattison, P., & Lazega, E. (2013). Exponential random graph models for multilevel networks. Social Networks, 35(1), 96–115.
Wang, W., Neuman, E. J., & Newman, D. A. (2014). Statistical power of the social network autocorrelation model. Social Networks, 38, 88–99.
White, D. R., Burton, M. L., & Dow, M. M. (1981). Sexual division of labor in African agriculture: A network autocorrelation analysis. American Anthropologist, 83, 824–849.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Bellotti, E., Guadalupi, L., Conaldi, G. (2016). Comparing Fields of Sciences: Multilevel Networks of Research Collaborations in Italian Academia. In: Lazega, E., Snijders, T. (eds) Multilevel Network Analysis for the Social Sciences. Methodos Series, vol 12. Springer, Cham. https://doi.org/10.1007/978-3-319-24520-1_9
Download citation
DOI: https://doi.org/10.1007/978-3-319-24520-1_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-24518-8
Online ISBN: 978-3-319-24520-1
eBook Packages: Social SciencesSocial Sciences (R0)