This paper reports on a study of local implementation in the UK Troubled Families Programme (TFP). Exploring the experiences of 12 families, the policies of local bureaucrats, and a critical reading of the literature, we argue that the local case represented an attempt to partially renegotiate disciplinary elements of the national programme and to recognise that the families were affected by structural poverty and inequality. Locating the TFP in the literature on disciplinary social policy, multi-scale 'Fast Policy' and the potential for local subversion through the agency of frontline workers, we suggest that that local attempts to renegotiate programme priorities were partially successful. These attempts were characteristic of 'contingent coping' in terms both of institutional processes and outcomes for the families involved. The evidence reported is significant and timely in the context of the expanded and relaunched TFP and this shapes our commentary on the recently published Improving Lives strategy.

Emerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services. Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation.

The acquisition and understanding of data is of paramount importance in any scientific context. However, the complexity of data due to its exponentially increasing size, its dynamical properties, and its internal contradictory information, raises huge challenges, which are at the core of Big Data science. In this paper, we discuss an automatic method to identify, rank and discover knowledge specifically focusing on Criminology research. Our main motivation is to create a set of tools to guide criminology experts through the decision process and knowledge discovery. In our validation we will show the clear potential of the proposed method.

No abstract

No abstract