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Law enforcement agencies must constantly make decisions about which crime problems to tackle. In the context of policing organised crime, these decisions can be extremely complex as strategic decision-makers have to make proactive judgements about priorities in a politically charged environment with finite resources. Yet most studies on this topic are concerned with intelligence products and their presentation in threat assessments, and they tend to downplay the significant role of a wide array of factors that influence strategic decisions. This perspective also overlooks the role of processes of social construction in setting priorities and how they can explain rises and falls of organised crime problems within strategic agendas.
The presentation aims to explore how police organisations set strategic priorities to tackle organised crime. By applying Best’s (2016) natural history model of social problems’ process the study examines changes and developments in the construction of organised crime problems over time, as well as the factors that drive these changes. A historical review of British organised crime policing reveals trends of centralisation and amalgamation, as well as a pattern of frequent institutional changes. These changes, however, facilitate ongoing processes of organisational inheritance which renders each agency dependent on its predecessors’ priorities and activities. Content analysis of annual reports by the four national organised crime fighting agencies in the UK is used to map out patterns and trends of change in strategic priorities; interviews with former strategic decision-makers are then used to illuminate behind-the-scenes processes of change. Findings demonstrate the various pathways of crime problem construction: some problems emerge and develop into high priorities, some remain persistent (in either high or low ranks) through time, and some fall from strategic agendas; crucially, these are not clear, linear trends but reinforce Best’s assertion of (2016) ‘cycles of concern’ across time.