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Knowledge Integration and Diffusion: Measures and Mapping of Diversity and Coherence

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Abstract

In this chapter, I present a framework based on the concepts of diversity and coherence for the analysis of knowledge integration and diffusion. Visualisations that help to understand insights gained are also introduced. The key novelty offered by this framework compared to previous approaches is the inclusion of cognitive distance (or proximity) between the categories that characterise the body of knowledge under study. I briefly discuss different methods to map the cognitive dimension.

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Notes

  1. 1.

    It should be noted that the evolutionary view of science and technology is prevalent both among constructivist sociologists such as Bijker (Pinch & Bijker, 1984) and positivist economists such as Dosi (1982).

  2. 2.

    The use of these five dimensions is an expedient simplification. One may easily conceive more dimensions within each of the dimensions listed, making a more fine-grained description of cognitive or social dimensions, for example.

  3. 3.

    To my knowledge, coherence had only been introduced in this single form, with intensity defined as the proportion of citations between WoS categories i ij  = p ij . The form of coherence I adopt in this chapter follows from Soós and Kampis (2012) rather than Rafols, Leydesdorff et al. (2012). In the latter, coherence, i.e. \( {\displaystyle \sum_{i,j\left(i\ne j\right)}{p}_{ij}}{d}_{ij} \) was normalised (divided) by Rao-Stirling diversity, i.e. \( {\displaystyle \sum_{i,j\left(i\ne j\right)}{p}_i{p}_j{d}_{ij}} \). Such normalisation was useful to remove the correlation between the two variables, but it seems unnecessarily complicated for a general framework.

  4. 4.

    This classification and underlying map can be downloaded and publicly used. It is available at http://sci.cns.iu.edu/ucsdmap/.

  5. 5.

    This classification is available at http://www.ludowaltman.nl/classification_system/.

  6. 6.

    According to Boyack, Klavans, Small and Ungar (2014), more than 99 % of clusters are stable at a level of aggregation of about 500 clusters for all science.

  7. 7.

    http://www.thevantagepoint.com/.

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Acknowledgements

This chapter summarises work carried out with many collaborators, in particular with L. Leydesdorff, A.L. Porter and A. Stirling. I am grateful to D. Chavarro for writing the code in R language to compute diversity. I thank Y.X. Liu, R. Rousseau and A. Stirling for fruitful comments. I acknowledge support from the UK ESRC grant RES-360-25-0076 (“Mapping the dynamics of emergent technologies”) and 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 author and do not necessarily reflect the views of the funders.

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Authors and Affiliations

  1. Ingenio (CSIC-UPV), Universitat Politècnica de València, València, (Spain)

    Ismael Rafols

  2. SPRU (Science Policy Research Unit), University of Sussex, Brighton, (UK)

    Ismael Rafols

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  1. Ismael Rafols
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Correspondence to Ismael Rafols .

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Editors and Affiliations

  1. School of Informatics and Computing, Indiana University, Bloomington, Indiana, USA

    Ying Ding

  2. University of Antwerp, Antwerp, Belgium

    Ronald Rousseau

  3. University of Wisconsin-Milwaukee, Milwaukee, Wisconsin, USA

    Dietmar Wolfram

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Rafols, I. (2014). Knowledge Integration and Diffusion: Measures and Mapping of Diversity and Coherence. In: Ding, Y., Rousseau, R., Wolfram, D. (eds) Measuring Scholarly Impact. Springer, Cham. https://doi.org/10.1007/978-3-319-10377-8_8

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