Bowling Together: Scientific Collaboration Networks of Demographers at European Population Conferences
Studies of collaborative networks of demographers are relatively scarce. Similar studies in other social sciences provide insight into scholarly trends of both the fields and characteristics of their successful scientists. Exploiting a unique database of metadata for papers presented at six European Population Conferences, this report explores factors explaining research collaboration among demographers. We find that (1) collaboration among demographers has increased over the past 10 years, however, among co-authored papers, collaboration across institutions remains relatively unchanged over the period, (2) papers based on core demographic subfields such as fertility, mortality, migration and data and methods are more likely to involve multiple authors and (3) multiple author teams that are all female are less likely to co-author with colleagues in different institutions. Potential explanations for these results are discussed alongside comparisons with similar studies of collaboration networks in other related social sciences.
KeywordsDemography Population studies Scientific collaboration Collaboration networks
We are grateful to the European Association for Population Studies (EAPS) and the PAMPA 5.1 supporting staff for supplying us the data in an electronic format. We are grateful for the suggestions provided by the Associate Editor and two anonymous reviewers.
- Burch, T. K. (2018). Data, models, theory and reality: The structure of demographic knowledge. In Model-based demography (pp. 21–42). Cham: Springer. https://doi.org/10.1007/978-3-319-65433-1_2.
- Goujon, A., Fürnkranz-Prskawetz, A., & Eder, J. (2015). 40 years of the Vienna Institute of Demography 1975–2015. From an Austrian to a European to a Global Player. Vienna: Vienna Institute of Demography. http://www.oeaw.ac.at/fileadmin/subsites/Institute/VID/PDF/Publications/diverse_Publications/VID_40years_Web_Final.pdf. Accessed 15 Jan 2018.
- Gu, Z. (2016). Circular visualization. https://github.com/jokergoo/circlize. Accessed 23 Jan 2018.
- Kahle, D., & Wickham, H. (2013). ggmap: Spatial Visualization with ggplot2. The R Journal, 5(1), 144–161.Google Scholar
- Merchant, E. K. (2015). Prediction and control—Global population, population science, and population politics in the twentieth century. Ann Arbor: University of Michigan. Retrieved from http://www.emilyklancher.com/digdemog/tmod/topicmod.html.
- Mullen, L., Blevins, C., & Schmidt, B. (2015). Predict gender from names using historical data. https://github.com/ropensci/gender. Accessed 15 Aug 2016.
- Raftery, A. E., Hoeting, J., Volinsky, C., Painter, I., & Yeung, K. Y. (2015). BMA: Bayesian model averaging. ftp://cran.r-project.org/pub/R/web/packages/BMA/BMA.pdf. Accessed 01 Nov 2016.