AbstractInterdisciplinary Research (IDR) has received a lot of attention from academics, policy-makers, and decision-makers alike. RCUK invests £3 billion in research grants each year (RCUK 2017); half of the grants are provided to investigators who hail from different departments. There is mounting awareness of the challenges facing IDR, and a large body of literature trying to establish how IDR can be analysed (Davidson 2015, Yegros-Yegros, Rafols et al. 2015). Of these, the majority have been qualitative studies and it has been noticed that there is a distinct lack of quantitative studies that can be used to identify how to enable IDR.
The literature shows that many of the barriers to IDR can be classified as either cultural or administrative (Katz and Martin 1997, Cummings and Kiesler 2005, Rafols 2007, Wagner, Roessner et al. 2011), neither of which are easily changed over a short period of time. The perspective taken in this research is that change can be affected by enabling the individuals who conduct IDR. Herein lies the main challenge; how can these future leaders of IDR be identified so that they can be properly supported.No existing datasets were deemed suitable for the purpose, and a new dataset was created to analyse IDR. To isolate dynamics within an organisation, hard boundaries were drawn around research-organisations. The University of Bath journal co-authorship dataset 2000-2017 was determined to be suitable for this purpose. From this dataset a co-authorship network was created. To analyse this, established models from literature were adapted and used to identify differences in disciplinary and interdisciplinary archetypes. This was done through a correlational study. No statistically significant differences between such author archetypes were found. It was therefore concluded that an alternative approach was necessary.By adapting the networks framework to account for different types of links between edges, a multilayer perspective was adopted. This resulted in a rank-3 tensor, node-aligned framework being proposed, allowing disciplines to be represented in the network. By using this framework to construct the University of Bath multiplex co-authorship network, an exemplar structure was established through use of a series of proposed structural metrics.A growth model was proposed and successfully recreated the structure and thereby uncovered mechanics affecting real-world multiplex networks. This highlighted the importance of node entities and the layer closeness centrality. This implies that it is very difficult to carry over benefits across disciplines, and that some disciplines are better suited to share and adapt knowledge than others. The growth model also allowed an analytical expression for the rate of change of disciplinary degree, thereby providing a model for who is most likely to enable and sustain IDR.
|Date of Award||25 Sep 2018|
|Supervisor||Linda Newnes (Supervisor) & Nigel Johnston (Supervisor)|
- Multilayer networks
- Interdisciplinary research
- Social network analysis