Interdisciplinary research and the production of local knowledge: evidence from a developing country
Diego Chavarro 1, Puay Tang 2, Ismael Rafols 3
, SPRU (Science and Technology Policy Research), University of Sussex - Falmer, Brighton - BN1 9SL - United Kingdom
(Corresponding author), SPRU (Science and Technology Policy Research), University of Sussex - Falmer, Brighton - BN1 9SL - United Kingdom
, Ingenio (CSIC-UPV), Universitat Politècnica de València & SPRU (Science and Technology Policy Research), University of Sussex
Working Paper (locally hosted)
Arxiv link: http://arxiv.org/abs/1304.6742
This paper examines the role of interdisciplinary research for the development of knowledge pertaining to local issues. Using Colombian publications from 1991 until 2011 in the Web of Science, we investigate the relationship between the degree of interdisciplinarity and the local focus of the articles. We find that a higher degree of interdisciplinarity in a publication is associated with a greater emphasis on local issues. In particular, our results support the view that research that combines cognitively disparate disciplines, what we refer to as Distal Interdisciplinarity, is associated with more local relevance of research. In contrast we find that research that involves a clear disciplinary focus with some, but limited engagement with neighbouring disciplines, generates less local knowledge. We conclude by arguing that public research initiatives that aim to appropriate the socio-economic benefits from publicly funded research should not focus exclusively on research excellence, which tends to be treated in disciplinary terms and citation counts as reflected in national research assessment exercises. Implications for policy are offered with attention to policies for capturing the societal benefits of publicly funded research, and implicitly, research assessment exercises.
Keywords: local knowledge; interdisciplinary research; evaluation; S&T capabilities; social relevance
1. Raw data in Excel format - link
2. Script in R and raw data in csv format to compute diversity measures (for instructions read the file readme.txt) - link
3. Further details on the quantitative analysis, including descriptive statistics and test of the robustness of the regression - link
We are grateful to Pablo d’Este, Alan Porter and Andy Stirling for fruitful discussions. We gratefully acknowledge support from the US National Science Foundation (Award #1064146 - “Revealing Innovation Pathways: Hybrid Science Maps for Technology Assessment and Foresight”). The findings and observations contained in this paper are those of the authors and do not necessarily reflect the views of the National Science Foundation.