Unusual suspects: identifying active serious offenders by self-selection policing.
Doctoral thesis, UCL (University College London).
The present thesis examines the emergent and complementary investigative method known as self-selection policing. This method seeks to identify minor offences indicative of more serious criminality, whereby the serious offender volunteers him or herself for warranted police attention by dint of committing a minor (often considered innocuous) infraction of the law. In early chapters a conceptual and theoretical underpinning for self-selection is developed by exploring relevant criminological and psychological theory. Terms and concepts are clarified early on, for example, discussion and clarity are provided regarding what constitutes serious and minor offences and offenders. Next, a series of exploratory studies is presented whereby specific minor offences are identified and their utility as indicators (or ‘flags’) for more serious criminality tested. These include non-compliance with Home Office Road Transport Form 1 (HO/RT1), where drivers are required to present necessary motoring documents to police within seven days, and the giving of false details to police. After presenting a theoreical and empirical case for using self-selection policing, late chapters explore anticipated obstacles to its wider implementation. For example, a study is presented which demonstrates a general overestimation of offence homogeneity by police. The implications of this finding for self-selection policing are discussed. The present thesis concludes by suggesting where self-selection policing sits both conceptually and theoretically within academic criminology, and within operational policing. For example, suggestions are offered as to how police and public might be convinced of the utility of self-selection policing and how it might be best integrated with mainstream policing.
|Title:||Unusual suspects: identifying active serious offenders by self-selection policing|
|Open access status:||An open access version is available from UCL Discovery|
|UCL classification:||UCL > School of BEAMS > Faculty of Engineering Science > Jill Dando Institute of Security and Crime|
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