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Scientometrics

, Volume 103, Issue 3, pp 897–922 | Cite as

Cohesive subgroups in academic networks: unveiling clique integration of top-level female and male researchers

  • Nadine V. Kegen
Article

Abstract

Social networks are said to have a positive impact on scientific development. Conventionally, it is argued that female and male researchers differ in access to and participation in networks and hence experience unequal career opportunities. Due to limited capacities of time and resources as well as homophily, top-level scientists may structure their contacts to reduce problems of complexity and uncertainty. The outcomes of the structuring can be cohesive subgroups within networks of relation. Women in science might suffer exclusion from cliques because of being dissimilar in the arena. The present paper aims to explore integration in and composition of scientific cliques. A three-step analysis is conducted: Firstly, cliques are identified. Secondly, overlap structures are examined. Thirdly, group compositions are analysed in terms of other personal attributes of the researchers involved. Building on network data of female and male investigators, the article applies a comparative case study design including two cutting edge research institutions from the German Excellence Initiative. The study contrasts a Cluster of Excellence with a Graduate School and the corresponding formal with the informal networks. The results imply that the general hypothesis of unfavourably embedded female researchers cannot be supported. Although women are less integrated in scientific cliques, the majority is involved in an inner social circle which enables access to career-relevant network resources.

Keywords

Clique analysis Cohesive subgroup Cutting edge research Formal and informal networks Social circle Women in science  

References

  1. Alba, R. D. (1973). A graph-theoretic definition of a sociometric clique. Journal of Mathematical Sociology, 3(1), 113–126.CrossRefMATHMathSciNetGoogle Scholar
  2. Alba, R. D., & Moore, G. (1978). Elite social circles. Sociological Methods and Research, 7(2), 167–188.CrossRefGoogle Scholar
  3. Allison, P. D., & Long, J. S. (1990). Departmental effects on scientific productivity. American Sociological Review, 55(4), 469–478.CrossRefGoogle Scholar
  4. Asmar, C. (1999). Is there a gendered agenda in academia? The research experience of female and male PhD graduates in Australian universities. Higher Education, 38(3), 255–273.CrossRefGoogle Scholar
  5. Beaufaÿs, S. (2012). Führungspositionen in der Wissenschaft: Zur Ausbildung männlicher Soziabilitätsregime am Beispiel von Exzellenzeinrichtungen. In S. Beaufaÿs, A. Engels, & H. Kahlert (Eds.), Einfach Spitze? Neue Geschlechterperspektiven auf Karrieren in der Wissenschaft (pp. 87–117). Frankfurt: Campus Verlag.Google Scholar
  6. Borgatti, S. P. (2002). Netdraw network visualization. Harvard: Analytic Technologies.Google Scholar
  7. Borgatti, S. P., & Everett, M. G. (1999). Models of core/periphery structures. Social Networks, 21(4), 375–395.CrossRefGoogle Scholar
  8. Borgatti, S. P., Everett, M. G., & Freeman, L. C. (2002). Ucinet for Windows: Software for social network analysis. Harvard: Analytic Technologies.Google Scholar
  9. Böröcz, J., & Southworth, C. (1998). “Who you know”: Earning effects of formal and informal social network resources under late state socialism in Hungary. Journal of Socio-Economics, 27(3), 401–425.CrossRefGoogle Scholar
  10. Bourdieu, P. (1986). The forms of capital. In J. G. Richardson (Ed.), Handbook of theory and research for the sociology of education (pp. 241–258). New York: Greenwood Press.Google Scholar
  11. Bozeman, B., & Corley, E. (2004). Scientists’ collaboration strategies: Implications for scientific and human capital. Research Policy, 33(4), 599–616.CrossRefGoogle Scholar
  12. Brass, D. J., Galaskiewicz, J., Greve, H. R., & Tsai, W. P. (2004). Taking stock of networks and organizations: A multilevel perspective. Academy of Management Journal, 47(6), 795–817.CrossRefGoogle Scholar
  13. Bron, C., & Kerbosch, J. (1973). Algorithm 457: Finding all cliques of an undirected graph. Communications of the ACM, 16(9), 575–579.CrossRefMATHGoogle Scholar
  14. Burt, R. S. (1983). Studying status/roles sets using mass surveys. In R. S. Burt & M. Minor (Eds.), Applied network analysis (pp. 100–118). Beverly Hills: Sage Publications.Google Scholar
  15. Burt, R. S. (2000). The network structure of social capital. Research in Organizational Behavior, 22, 345–423.CrossRefGoogle Scholar
  16. Burt, R. S., & Ronchi, D. (1994). Measuring a large network quickly. Social Networks, 16(2), 91–135.CrossRefGoogle Scholar
  17. Chang, H.-W., & Huang, M.-H. (2014). Cohesive subgroups in the international collaboration network in astronomy and astrophysics. Scientometrics, 101(3), 1587–1607.CrossRefGoogle Scholar
  18. Coleman, J. S. (1988). Social capital in the creation of human capital. The American Journal of Sociology, 94, 95–120.CrossRefGoogle Scholar
  19. Coleman, J. S. (1990). Foundations of social theory. Cambridge: Harvard University Press.Google Scholar
  20. Collins, R. (1988). Theoretical sociology. San Diego: Harcourt Brace Jovanovich.Google Scholar
  21. de Nooy, W., Mrvar, A., & Batagelj, V. (2005). Exploratory social network analysis with Pajek. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  22. Deutsche Forschungsgemeinschaft. (2012). Förderatlas 2012: Kennzahlen zur öffentlich finanzierten Forschung in Deutschland (Vol. 1). Weinheim: Wiley-VCH Verlag.Google Scholar
  23. Deutsche Forschungsgemeinschaft. (2013). Excellence initiative at a glance: The programme by the German Federal and State Governments to promote top-level research at universities. The second phase 20122017: Graduate schoolsClusters of excellenceInstitutional strategies (Vol. 5). Bonn: DFG.Google Scholar
  24. Doreian, P. (1970). Mathematics and the study of social relations. London: Weidenfeld & Nicolson.Google Scholar
  25. Duysters, G. M., Hagedoorn, J., & Lemmens, C. E. A. V. (2002). The effect of alliance block membership on innovative performance. Working paper, 02.06. Eindhoven Centre for Innovation Studies.Google Scholar
  26. Duysters, G. M., & Lemmens, C. E. A. V. (2002). Cohesive subgroup formation: Enabling and constraining effects of social capital in strategic technology alliance networks. Working paper, 02.07. Eindhoven Centre for Innovation Studies.Google Scholar
  27. Eisenhardt, K. M. (1989). Building theories from case study research. Academy of Management Review, 14(4), 532–550.Google Scholar
  28. Engels, A., Ruschenburg, T., & Zuber, S. (2012). Chancengleichheit in der Spitzenforschung: Institutionelle Erneuerung der Forschung in der Exzellenzinitiative des Bundes und der Länder. In T. Heinze & G. Krücken (Eds.), Institutionelle Erneuerungsfähigkeit der Forschung (pp. 187–217). Wiesbaden: Springer VS.CrossRefGoogle Scholar
  29. Etzkowitz, H., Kemelgor, C., & Uzzi, B. (2000). Athena unbound: The advancement of women in science and technology. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  30. European Commission. (2012). She figures 2012: Gender in research and innovation. Luxembourg: EC.Google Scholar
  31. Evans, T. S. (2010). Clique graphs and overlapping communities. Journal of Statistical Mechanics: Theory and Experiment 2010(12), 1–21.Google Scholar
  32. Everett, M. G., & Borgatti, S. P. (1998). Analyzing clique overlap. Connections, 21(1), 49–61.Google Scholar
  33. Feeney, M. K., & Bernal, M. (2010). Women in STEM networks: Who seeks advice and support from women scientists? Scientometrics, 85(3), 767–790.CrossRefGoogle Scholar
  34. Frank, K. A. (1995). Identifying cohesive subgroups. Social Networks, 17(1), 27–56.CrossRefGoogle Scholar
  35. Freeman, L. C. (1979). Centrality in social networks: Conceptual clarification. Social Networks, 1(1), 215–239.MathSciNetGoogle Scholar
  36. Gargiulo, M., & Benassi, M. (2000). Trapped in your own net? Network cohesion, structural holes, and the adaptation of social capital. Organization Science, 11(2), 183–196.CrossRefGoogle Scholar
  37. Gersick, C. J. G., Bartunek, J. M., & Dutton, J. E. (2000). Learning from academia: The importance of relationships in professional life. Academy of Management Journal, 43(6), 1026–1044.CrossRefGoogle Scholar
  38. Harary, F. (1969). Graph theory. Reading: Addison-Wesley.Google Scholar
  39. Hennig, M., Brandes, U., Pfeffer, J., & Mergel, I. (2012). Studying social networks: A guide to empirical research. Frankfurt: Campus Verlag.Google Scholar
  40. Higley, J., Desley, D., & Smart, D. (1979). Elites in Australia. London: Routledge & Kegan Paul.Google Scholar
  41. Higley, J., & Moore, G. (1981). Elite integration in the United States and Australia. The American Political Science Review, 75(3), 581–597.CrossRefGoogle Scholar
  42. Holland, P. W., & Leinhardt, S. (1973). The structural implications of measurement error in sociometry. Journal of Mathematical Sociology, 3(1), 85–111.CrossRefMATHGoogle Scholar
  43. Hollstein, B. (2008). Netzwerke, Akteure und Bedeutungen: Zur Integration qualitativer und quantitativer Verfahren in der Netzwerkforschung. In K.-S. Rehberg (Ed.), Die Natur der Gesellschaft: Verhandlungen des 33. Kongresses der Deutschen Gesellschaft für Soziologie in Kassel 2006 (pp. 3359–3370). Frankfurt: Campus Verlag.Google Scholar
  44. Jansen, D. (2006). Einführung in die Netzwerkanalyse: Grundlagen, Methoden, Forschungsbeispiele (Vol. 3). Wiesbaden: Springer VS.Google Scholar
  45. Jansen, D. (2008). Research networks—Origins and consequences: First evidence from a study of Astrophysics, Nanotechnology and Micro-economics. In M. Albert, D. Schmidtchen, & S. Voigt (Eds.), Scientific competition (pp. 209–230). Tübingen: Mohr Siebeck.Google Scholar
  46. Jansen, D., von Görtz, R., & Heidler, R. (2010). Knowledge production and the structure of collaboration networks in two scientific fields. Scientometrics, 83(1), 219–241.CrossRefGoogle Scholar
  47. Kadushin, C. (1966). The friends and supporters of psychotherapy: On social circles in urban life. American Sociological Review, 31(6), 786–802.CrossRefGoogle Scholar
  48. Kadushin, C. (1968). Power, influence and social circles: A new methodology for studying opinion makers. American Sociological Review, 33(5), 685–699.CrossRefGoogle Scholar
  49. Katz, J. S., & Martin, B. R. (1997). What is research collaboration? Research Policy, 26(1), 1–18.CrossRefGoogle Scholar
  50. Kilduff, M., & Mehra, A. (1996). Hegemonic masculinity among the elite: Power, identity, and homophily in social networks. In C. Cheng (Ed.), Masculinities in organizations (pp. 115–129). Thousand Oaks: Sage Publications.Google Scholar
  51. Knoke, D., & Burt, R. S. (1983). Prominence. In R. S. Burt & M. J. Minor (Eds.), Applied network analysis: A methodological introduction (pp. 195–222). Berverly Hills: Sage Publications.Google Scholar
  52. Knoke, D., & Kuklinski, J. H. (1982). Network analysis (Vol. 1). Beverly Hills: Sage Publications.Google Scholar
  53. Kossinets, G. (2006). Effects of missing data in social networks. Social Networks, 28(3), 247–268.CrossRefGoogle Scholar
  54. Luce, R. D., & Perry, A. D. (1949). A method of matrix analysis of group structure. Pychometrika, 14(2), 95–116.CrossRefMathSciNetGoogle Scholar
  55. Maranto, C. L., & Griffin, A. E. C. (2011). The antecedents of a ‘chilly climate’ for women faculty in higher education. Human Relations, 64(2), 139–159.CrossRefGoogle Scholar
  56. Mauleón, E., & Bordons, M. (2010). Male and female involvement in patenting activity in Spain. Scientometrics, 83(3), 605–621.CrossRefGoogle Scholar
  57. McPherson, M., Smith-Lovin, L., & Cook, J. M. (2001). Birds of a feather: Homophily in social networks. Annual Review of Sociology, 27(1), 415–444.CrossRefGoogle Scholar
  58. Moore, G. (1979). The structure of a national elite network. American Sociological Review, 44(5), 673–692.CrossRefGoogle Scholar
  59. Moore, G. (1988). Women in elite positions: Insiders or outsiders? Sociological Forum, 3(4), 566–585.CrossRefGoogle Scholar
  60. Padgett, J. F., & Ansell, C. K. (1993). Robust action and the rise of the Medici, 1400–1434. American Journal of Sociology, 98(6), 1259–1319.CrossRefGoogle Scholar
  61. Parker, A., & Arthur, M. B. (2000). Careers, organizing, and community. In M. A. Peiperl, M. B. Arthur, R. Coffee, & T. Morris (Eds.), Career frontiers: New conceptions of working lives (pp. 99–121). New York: Oxford University Press.Google Scholar
  62. Pfeffer, J. (1985). Organizational demography: Implications for management. California Management Review, 28(1), 67–81.CrossRefMathSciNetGoogle Scholar
  63. Podolny, J. M., & Baron, J. N. (1997). Resources and relationships: Social networks and mobility in the workplace. American Sociological Review, 62(5), 673–693.CrossRefGoogle Scholar
  64. R, Foundation for Statistical Computing. (2013). R: A language and environment for statistical computing. Vienna: R-Foundation for Statistical Computing.Google Scholar
  65. Robins, G., Pattison, P., & Woolcock, J. (2004). Missing data in networks: Exponential random graph (p*) models for networks with non-respondents. Social Networks, 26(3), 257–283.CrossRefGoogle Scholar
  66. Ruschenburg, T., Zuber, S., Engels, A., & Beaufaÿs, S. (2011). Frauenanteile in der Exzellenzinitiative: Zu den methodischen Herausforderungen bei der Ermittlung aussagekräftiger Vergleichswerte. Die Hochschule, 2, 161–172.Google Scholar
  67. Šadl, Z. (2009). ‘We women are no good at it’: Networking in academia. Sociologicky Casopis-Czech Sociological Review, 45(6), 1239–1263.Google Scholar
  68. Schneider, B. (1987). The people make the place. Personell Psychology, 40(3), 437–453.CrossRefGoogle Scholar
  69. Scott, J. (2013). Social network analysis (Vol. 3). Los Angeles: Sage Publications.Google Scholar
  70. Simmel, G. (1968). Soziologie: Untersuchungen über die Formen der Vergesellschaftung (Vol. 5). Berlin: Duncker & Humblot.Google Scholar
  71. Statistisches Bundesamt. (2012). Bildung und Kultur: Personal an Hochschulen 2011. Wiesbaden: SB.Google Scholar
  72. Storey, K., & Provost, N. (1996). The use of clique analysis to assess integration changes in a supported employment setting. Exceptionality: A Special Education Journal, 6(2), 111–123.CrossRefGoogle Scholar
  73. Täube, V. G. (2008). Local social capital in unfolding structures. In U. Serdült, & V. G. Täube (Eds.), Applications of social network analysis (pp. 61–74). Berlin: Wissenschaftlicher Verlag Berlin.Google Scholar
  74. Tichy, N. (1973). An analysis of clique formation and structure in organizations. Administrative Science Quarterly, 18(2), 194–208.CrossRefGoogle Scholar
  75. Uzzi, B. (1997). The social structure and competition in interfirm networks: The paradox of embeddedness. Administrative Science Quarterly, 42(1), 35–67.CrossRefGoogle Scholar
  76. van Emmerik, I. H. (2006). Gender differences in the creation of different types of social capital: A multilevel study. Social Networks, 28(1), 24–37.CrossRefGoogle Scholar
  77. Wang, D. J., Shi, X., McFarland, D. A., & Leskovec, J. (2012). Measurement error in network data: A re-classification. Social Networks, 34(4), 396–409.CrossRefGoogle Scholar
  78. Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications. Cambridge: Cambridge University Press.CrossRefGoogle Scholar

Copyright information

© Akadémiai Kiadó, Budapest, Hungary 2015

Authors and Affiliations

  1. 1.Department of Social SciencesUniversität HamburgHamburgGermany

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