Impact and the Reflexive Imperative in Criminal Justice Policy, Practice and Research

Abstract

This volume grows out of two parallel but distinct developments in social science research that affect the way researchers study and seek to have an impact in the areas of crime and criminal justice. These are the increasing acceptance and practice of (some form of) reflexivity in social science research, on the one hand, and, on the other, the changing context of research itself. On the latter point, we note that criminologists working across different jurisdictions are experiencing heightened pressures to render their research relevant and appealing to external audiences. These pressures are linked in part with the fact that governments in Australia, the UK and the USA (along with other countries) are increasingly keen to ensure that their investment in the higher education sector is delivering ‘value for money’. This implies that research and teaching activities that are government-funded must increasingly align with, or at least demonstrate alignment with, what these governments define as the public interest. In Australia, for example, the Australian Research Council, which is responsible for administering public research funding, has identified a list of nine strategic ‘Science and Research Priorities’ to organise funding of ‘support for science and research on the most important challenges facing Australia’ (developed partly from a 2014 white paper ‘Boosting the commercial returns of research’; see ARC 2016). With the possible exception of ‘cybersecurity’, none of these strategic priorities appear to be directly relevant to criminology or indeed, the social sciences. The specified research priorities relate primarily to what are known as ‘STEM’ subjects (Science, Technology, Engineering and Medicine), thereby prioritising an increasingly narrow set of subjects and research methodologies that reflect a pragmatic and in our view myopic governmental understanding of what constitutes societal value. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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