Georg Wenzelburger

When it comes to the relationship between social policy and penal policy, existing scholarship often focuses on the penal–welfare tradeoff, according to which countries with large and generous welfare states tend to have lower incarceration rates and less harsh treatment of offenders. We know much less about the relationship between the punitive turn in criminal justice and the use of discipline within social policy. Has there been a parallel trend of law-and-order policies and stricter benefit conditionality, a kind of ‘criminalization’ of welfare beneficiaries, as critical scholarship suggests? We test this idea for the first time with quantitative data, using public spending on public order and safety and unemployment benefit conditionality data for 18 rich democracies between 1990 and 2012, that is, the period when a punitive turn as well as the rise of activation and workfare is said to have taken place. Contrary to the critical literature, we do not find evidence of parallel trends toward more discipline in both areas, but rather a negative relationship of ‘communicating vessels’, where a greater use of disciplinary tools in social policy is associated with stagnating or even shrinking spending on police and prisons. Moreover, this pattern tends to emerge under conditions of higher welfare state generosity. These findings have important implications about the role of state ‘discipline’ in contemporary policymaking.

Algorithms increasingly govern people's lives, including through rapidly spreading applications in the public sector. This paper sheds light on acceptance of algorithms used by the public sector emphasizing that algorithms, as parts of socio‐technical systems, are always embedded in a specific social context. We show that citizens' acceptance of an algorithm is strongly shaped by how they evaluate aspects of this context, namely the personal importance of the specific problems an algorithm is supposed to help address and their trust in the organizations deploying the algorithm. The objective performance of presented algorithms affects acceptance much less in comparison. These findings are based on an original dataset from a survey covering two real‐world applications, predictive policing and skin cancer prediction, with a sample of 2661 respondents from a representative German online panel. The results have important implications for the conditions under which citizens will accept algorithms in the public sector.