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Polarization in the social sciences: Assortative mixing in social science collaboration networks is resilient to interventions

Leifeld, Philip (2018) 'Polarization in the social sciences: Assortative mixing in social science collaboration networks is resilient to interventions.' Physica A: Statistical Mechanics and its Applications, 507. 510 - 523. ISSN 0378-4371

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Academic collaboration in the social sciences is characterized by a polarization between hermeneutic and nomological researchers. This polarization is expressed in different publication strategies. The present article analyzes the complete co-authorship networks in a social science discipline in two separate countries over five years using an exponential random graph model. It examines whether and how assortative mixing in publication strategies is present and leads to a polarization in scientific collaboration. In the empirical analysis, assortative mixing is found to play a role in shaping the topology of the network and significantly explains collaboration. Co-authorship edges are more prevalent within each of the groups, but this mixing pattern does not fully account for the extent of polarization. Instead, a thought experiment reveals that other components of the complex system dampen or amplify polarization in the data-generating process and that microscopic interventions targeting behavior change with regard to assortativity would be hindered by the resilience of the system. The resilience to interventions is quantified in a series of simulations on the effect of microscopic behavior on macroscopic polarization. The empirical study controls for geographic proximity, supervision, and topical similarity (using a vector space model), and the interplay of these factors is likely responsible for this resilience. The paper also predicts the co-authorship network in one country based on the model of collaborations in the other country.

Item Type: Article
Uncontrolled Keywords: Scientific collaboration; Co-authorship network; Polarization; Assortative mixing; Exponential random graph model; Network intervention; Social sciences
Divisions: Faculty of Social Sciences > Government, Department of
Depositing User: Elements
Date Deposited: 29 Aug 2019 09:53
Last Modified: 29 Aug 2019 09:53

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