Abstract

We contribute to the debate on societal impact of SSH by developing a methodology that allows a fine-grained observation of social groups that make use, directly or indirectly, of the results of research. We develop a lexicon of users with 76,857 entries, which saturates the semantic field of social groups of users and allows normalization. We use the lexicon in order to filter text structures in the 6637 impact case studies collected under the Research Excellence Framework in the UK. We then follow the steps recommended by Börner et al. (Annu Rev Inf Sci Technol 37:179–255, 2003) to build up visual maps of science, using co-occurrence of words describing users of research. We explore the properties of this novel kind of maps, in which science is seen from the perspective of research users.

Highlights

  • As discussed by several authors, societal impact has become one of the criteria of ex ante project selection in many institutions and countries (Kanninen and Lemola 2006; Donovan 2011; Dance 2013; Atkinson 2014; Penfield et al 2014)

  • The human effort is applied to the more value-added task of searching for pre-existing lexicons describing the entities. Both approaches have advantages and disadvantages: here we focus on the fact that using an external lexicon makes it hard to compute the recall of the approach

  • By annotating the entire corpus with the entries of the lexicon we find that the total number of words referring to target groups is 169,037, while the number of different target groups is

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Summary

Introduction

As discussed by several authors, societal impact has become one of the criteria of ex ante project selection in many institutions and countries (Kanninen and Lemola 2006; Donovan 2011; Dance 2013; Atkinson 2014; Penfield et al 2014). Keywords Impact assessment · Societal impact · Science map · Supervised text mining · Research users · Lexicon

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