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Social studies of scholarly life with sensor-based ethnographic observations

Scientometrics volume 119, pages 1387–1428 (2019)Cite this article

Abstract

Social network analysis is playing an increasingly important role in sociological studies. At the same time, new technologies such as wearable sensors make it possible to collect new types of social network data. We employed RFID tags to capture face-to-face interactions of participants of two consecutive Ph.D. retreats of a graduate school on climate research. We use this data in order to explore how it may support ethnographic observations and to gain further insights on scholarly interactions. The unique feature of the data is the opportunity to distinguish short and long conversations, which often have a different nature from a sociological point of view. Furthermore, an advantage of this data is the availability of socio-demographic, research-related, and situational attributes of participants. We show that, even though an interaction partner is often found rather randomly during coffee breaks of retreats, a strong homophily between participants from the same institutions or research areas exists. We identify cores of the networks and participants who play ambassador roles between communities, e.g., persons who visit the retreat for the second time are more likely to be ambassadors. Overall, we show the usefulness and potential of RFID tags for scientometric studies.

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Notes

  1. http://www.sociopatterns.org.

  2. In general, both RFID readers and servers can be connected to LAN, but for our experiments we had to set up our own infrastructure due to the lack of LAN.

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Acknowledgements

We thank Christoph Scholz and Björn Fries for helping to collect RFID data during the retreats. This work has been partially supported by Germany’s Excellence Strategy (DFG EXC 177 CliSAP) and the German Research Foundation (DFG) project “MODUS” (Grant AT 88/4-1).

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Authors and Affiliations

  1. Interdisciplinary Research Center for Information System Design (ITeG), Knowledge and Data Engineering Group, University of Kassel, Wilhelmshöher Allee 73, 34121, Kassel, Germany

    Mark Kibanov & Gerd Stumme

  2. Socium, University of Bremen, Mary-Somervile Str. 9, 28205, Bremen, Germany

    Raphael H. Heiberger

  3. Department of Social Sciences, University of Hamburg, Grindelberg 5, 20144, Hamburg, Germany

    Simone Rödder

  4. Department of Cognitive Science and Artificial Intelligence, Tilburg University, Warandelaan 2, 5037 AB, Tilburg, The Netherlands

    Martin Atzmueller

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  1. Mark Kibanov
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  2. Raphael H. Heiberger
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  4. Martin Atzmueller
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Correspondence to Mark Kibanov.

Appendices

Appendix 1: Homophily

For the conference in 2015 we could only consider 2 days, i.e., can calculate a normal ERGM in which we consider the ties of the first day of the retreat. As we mentioned in “Data collection” Section, only few connections were captured during the last day of the 2015 retreat as same tags were put together and we had to remove some captured interactions as we could not distinguish between real interactions and tags that were just lying near each other. Therefore, the inversion of the time effect could not be reproduced since only few ties are overlapping in conference day 1 and day 2. We find no gender effect in the 2015 conference, even for longer interactions. All other results in Table 8 support the findings for the 2014 conference. The same is true given the stability of effects considering longer interactions (Fig. 15).

Table 8 Results of exponential random graph models for the convent in 2015, all interactions
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Fig. 15
figure 15

Goodness-of-fit measures for different network properties of TERGM models, all interactions. Box plots represent simulated estimates, solid lines empirical values. The closer lines are to estimates’ means and their 95% confidence intervals (i.e., within whiskers), the better the fit of the model for the respective value. a Gives goodness-of-fit for the full model C of the TERGM for the convent in 2014, b for the full model B of the ERGM for 2015

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Appendix 2: Structure of cores of the networks

See Fig. 16.

Fig. 16
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Properties of members of maximal connected component dependent on threshold

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Appendix 3: Roles of participants

Fig. 17
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Roles of participants of 2014 retreat depending on their gender related to different types of communities

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Fig. 18
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Roles of participants of 2014 retreat depending on their country of birth related to different types of communities

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Fig. 19
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Roles of participants of 2014 retreat depending on their status related to different types of communities

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Fig. 20
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Roles of participants of 2014 retreat depending on their affiliation with the host university related to different types of communities

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Fig. 21
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Roles of participants of 2015 retreat depending on their gender related to different types of communities

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Fig. 22
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Roles of participants of 2015 retreat depending on their country of birth related to different types of communities

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Fig. 23
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Roles of participants of 2015 retreat depending on their academic status related to different types of communities

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Roles of participants of 2015 retreat depending on their affiliation with the university of Hamburg related to different types of communities

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Fig. 25
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Roles of participants of 2015 retreat depending on whether they visited retreat 2014 related to different types of communities

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Kibanov, M., Heiberger, R.H., Rödder, S. et al. Social studies of scholarly life with sensor-based ethnographic observations. Scientometrics 119, 1387–1428 (2019). https://doi.org/10.1007/s11192-019-03097-w

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Keywords

  • Social network analysis
  • Face-to-face interaction
  • RFID
  • Sociology of science
  • Mixed methods