Collection of documents on the digitisation of higher education and research in Switzerland (1998-2020)

This project investigates the discourse about digitization of higher education and research in Swiss policy debates. In general, the discourse about higher education and research has been fundamentally shaped by digitization in the last decade. Universities, scientific academies, business groups and state actors formulated digital strategies and action plans to cope with the “chances and challenges of digitization for higher education and research”, as one report by the SERI stated. The debate goes far beyond the narrower field of the data sciences but marks it in various respects as a "strategic research area" (ETH Board 2016) or a fundamental "enabling technology" (SERI 2017). The discussion about digitization is part of sociotechnical imaginaries: Political, economic, and scientific actors create visions of the future in which social relations of and to digital technologies are described and framed (Jasanoff 2015; Jasanoff & Kim 2015; Meyer 2020). The future scenarios designed in the context of the digitization discourse are analysed as a case study of a collective conception of society based on statements by political, economic, and scientific actors. The formulation of political strategies and goals and the adoption of measures involve both discursive and non-discursive practices: By outlining the future development of societal domains, political actors also value and allocate attention, financial and other resources (Beckert 2016; Jasanoff 2015). The data basis for the study is formed by strategies documents and reports by actors in Swiss higher education and research policy (N=34). The period of the documents investigated ranges from 1998 to 2020, with most of them published after 2014. Since the documents from 2014 onward increasingly address "Big Data" and "Data Science" as well as their legal, economic, and educational aspects in education and research policy, this period forms the focus of the analysis. All documents were coded and analysed using qualitative content analysis to identify the relevant topics and social, i.e. political, economic, or technological dimensions in the corpus.