Measuring and fostering interdisciplinarity: social GM vs. natural evolution

Recently, interdisciplinary research has received significant media attention. Governments and universities encourage interdisciplinary research, but various reports show that reality might be not so bright: research proposals that belong to several fields of research have lower funding success; governments are still figuring out how to access quality of interdisciplinary research outputs. What could help overcome these challenges? Our argument is simple: decisions about interdisciplinary research should be informed by a solid understanding of the (inter)disciplines and interdisciplinary work. What is “discipline” and what is “inter”? What does it mean to work in interdisciplinary boundaries? How do new disciplines and research fields emerge? To which extend methods from evolutionary biology could be applicable for “measuring” interdisciplinarity? Below is a copy our comment that we wrote in response to this paper published recently in Nature.

Video from the University of Sydney Charles Perkins Centre:

Understanding and stimulating interdisciplinary research can benefit from having reliable methods to measure its benefits and the challenges it faces. It is useful to be reminded that assessments of interdisciplinary research proposals often involve extra hurdles (Nature 534, 589–590; 2016) and to see how metrics from evolutionary biology can assist in detecting some anomalies (Nature 534, 684–687; 2016). However, the ‘species’ of research represented in schemes like The 2008 Australian and New Zealand Standard Research Classification do not evolve naturally. Indeed, such classification schemes are periodically modified to recognize and support new emerging (interdisciplinary) fields of R&D. For example, the 2008 ANZSRC recognizes a number of established and emerging interdisciplinary research fields, such as nanotechnology, interdisciplinary engineering, cognitive science and the learning sciences. Theoretical and methodological diversity and complexity within such interdisciplinary fields can be even higher than across other independently classified research fields. (Note, our examination of the raw data published in Nature (534, 684–687; 2016) shows that research proposal success rates in these four explicitly interdisciplinary fields are higher than in their ‘parent’ fields.) While the notion that disciplines are naturally evolving species can be useful in suggesting new ways of measuring interdisciplinarity, we must not forget that disciplines and classification systems are dynamic social constructs. One might say that research activity is open to ‘genetic modification’, taxonomies are renegotiated and – in subtle ways – taxonomies reshape the evolution of that which they classify. We are not arguing that current interdisciplinary research funding schemes do not need improvement, but if we are to be smart about assessing and fostering interdisciplinary, then we must pay close attention to the distinctly social nature of evolution in research.


Bromham, L., Dinnage, R., & Hua, X. (2016). Interdisciplinary research has consistently lower funding success. [Letter]. Nature, 534(7609), 684-687. doi: 10.1038/nature18315. Retrieved from

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