Abstract
It is well-established that the process of learning and capability building is core to economic development and structural transformation. Since knowledge is ‘sticky’, a key component of this process is learning-by-doing, which can be achieved via a variety of mechanisms including international research collaboration. Uncovering significant inter-country research ties using Scopus co-authorship data, we show that within-region collaboration has increased over the past five decades relative to international collaboration. Further supporting this insight, we find that while communities present in the global collaboration network before 2000 were often based on historical geopolitical or colonial lines, in more recent years they increasingly align with a simple partition of countries by regions. These findings are unexpected in light of a presumed continual increase in globalisation, and have significant implications for the design of programmes aimed at promoting international research collaboration and knowledge diffusion.
Highlights
Following advances in transportation and communication technology over the past centuries, we’re witnessing a rise in global interactions both in terms of cross-border trade and investment as well as flows of people and information
In order to further investigate the rate of change of the modular structure over time, and the observed ‘regionalisation’ of research collaboration ties, we wish to quantify the similarity between each partition and its preceding partition, and between each partition and the ‘continental partition’
We address this research gap through analysing a worldwide dataset of international scientific publications spanning all major disciplines over five decades
Summary
Following advances in transportation and communication technology over the past centuries, we’re witnessing a rise in global interactions both in terms of cross-border trade and investment as well as flows of people and information. We apply a range of sophisticated methods deriving from network science and mathematical modelling, including historical profile clustering, calculation of the entropy of collaborations, community detection, and mutual information comparisons, which allow us to uncover patterns that have previously remained opaque.
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