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

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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.

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Notes

  1. Network actors must not necessarily be persons. They can also e.g., represent whole organisations or even national economies. In this paper, the actors are individual scientists.

  2. Emotional resources are defined to comprise friendship, propinquity, trust and advice.

  3. Hard social capital is defined to consist of accumulated task-oriented resources.

  4. The connections may be either direct (face-to-face) or indirect (via intermediaries).

  5. Besides, literature on social networks has revealed various other approaches to identify subgroup structures (e.g. n-cliques or k-plexes).

  6. It was funded by the Federal Ministry of Education and Research (BMBF) (Grant No.: 01FP0719) as well as European Social Fund (ESF) of the European Union. Any opinions expressed here are those of the author.

  7. Neither human nor animal rights were violated with this survey method.

  8. It was conducted by the former project associate Tina Ruschenburg.

  9. The third line refers to Institutional Strategies.

  10. These PIs were surveyed conjointly but separated later.

  11. At the beginning, 35 (later 27) GSs and CEs as well as 5 universities with Institutional Strategies participated in the whole study.

  12. When a connection between two researchers contains more than one exchanged resource (e.g. one formal and one informal), their relationship is said to be multiplex.

  13. All cell values which equalled or exceeded 1 were coded 1, the rest retained noted a 0.

  14. This routine checked all cells right above the diagonal against the corresponding cells left below and infixed the greater values in the respective cells.

  15. On the other hand, investigators that interact with each other formally may be expected to participate more likely in informal relationships with their colleagues than those who are not involved in formal ties.

  16. Regression analyses that included eight out of eleven excellence institutions were conducted. The results from this broader sample were presented at the closing conference of the underlying project in April 2013 and will be published in a final project book this year (2015).

  17. They could check “other” once again.

  18. The enumerator of the measure corresponds to Tichy’s clique characteristic openness.

  19. The programming was conducted by Daniel Gotthardt.

  20. Meanwhile, the PI who is highest in clique centrality belongs to the CE and is of male sex. The value occurs at the formal network and accounts for 39.

  21. Compared to clique analysis, particularly the absence of ties between the generated blocks is telling for identification of social structures.

  22. Such a matrix can also be obtained by usage of the Bron–Kerbosch-routine.

  23. The diagonal entries depict the size of the cliques.

  24. Weighted clique graphs reflect different amounts of overlap between the subgroups, referring to the number of actors that are common to both cliques considered.

  25. The program R has been used here too.

  26. Utilising the a-b-c matrix, the subgroups are run through per loop. It is checked whether a particular chosen group overlaps with another according to the criterion. If so, the groups are merged.

  27. The number of six arises as a result because two PIs have the same centrality value among the five most centrals.

  28. Note that the percentages in the collaboration network of the cluster at significantly isolated cliques are means of four subgroups at a time.

  29. In the project, there is more recent data on percentages of female PIs (surveyed in 2011). That data might exhibit slightly less female isolation. However, the increase in number of women implies that contemporary patterns would not be utterly different.

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  1. Department of Social Sciences, Universität Hamburg, Allende-Platz 1, 20146, Hamburg, Germany

    Nadine V. Kegen

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Correspondence to Nadine V. Kegen.

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This thesis is based on a shorter paper presentation at the 8th European Conference on Gender Equality in Higher Education, Vienna (2014).

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Kegen, N.V. Cohesive subgroups in academic networks: unveiling clique integration of top-level female and male researchers. Scientometrics 103, 897–922 (2015). https://doi.org/10.1007/s11192-015-1572-z

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