HSR Vol. 45 (2020) No. 3: Social Finance, Impact Investing, and the Financialization of the Public Interest.
Special Issue - Eve Chiapello & Lisa Knoll (Eds.): Social Finance, Impact Investing, and the Financialization of the Public Interest.
This HSR Special Issue compiles social science research on a relatively new socio-economic phenomenon: Social Finance and Impact Investing. Its promise is to lure private investment and/ or philanthropic capital into the sphere of social welfare. To do so, social wellbeing and social impact become subjects of financialization, entrepreneurialization, and quantification. The venture capital framework and financial principles of due diligence enter the world of the social sector – be it through new impact rating schemes, social responsibility documentation, or innovative welfare state funding schemes such as Social Impact Bonds. This Special Issue brings together empirical research on different cases (public-private development finance, social impact and venture investors, impact rating schemes, social impact bonds, and welfare state reforms) from diverse places (South Africa, Kenya, Italy, the US, the UK, and France).
Forum - Lilli Braunisch, Malte Schweia, Peter Graeff & Nina Baur (Eds.): Challenges for Big Data Analysis. Data Quality and Data Analysis of Analogous and Digital Mass Data.
While big data are one of the oldest social-science data types, their use in research practice has re-surged in recent decades due to IT innovations and the emergence of Web 2.0. However, research practice and the methodological discourse on big data fall apart within the scientific community: 1) Social science methodology focusses primarily on data quality. The debate in social science communities is almost 150 years old and has revealed the specific strengths and weaknesses of both big data and research-elicited data and also resulted in recommendations on how to handle each data type. 2) Computational social sciences primarily focus on data analysis, especially on new analysis techniques and algorithms for evaluating big data. Both research lines are hardly connected, and both have mutual blind spots. This HSR Forum therefore aims at overcoming this divide. It brings up the discussion of how these research lines can complement each other and can be improved by using the findings of each other.