Temporal distance and procedural justice in police misconduct

Mr Timothy Cubitt1

1NSW Police Force, , ,

2Western Sydney University, ,

The term ‘temporal distance’, in the field of police misconduct, refers to the distance from maladaptive behaviour to remediation of that behaviour. It is generally considered that the further into the future the ramifications of an action lie, the more easily an employee will engage in similar behaviours. There is a strong body of work considering the role of risk taking as temporal distance increases, indicating proportionality between the time delay of remediation and an increasing propensity for deviant behaviours. It does not appear that this is a function of the riskier option, in this circumstance misconduct, becoming more attractive, rather temporal distance appears to reduce the attraction to safer behavioural options through degrading the perceived probability of a negative outcome occurring. In conjunction with this effect, individuals with a negative affect toward a process, as may be present in the police complaint process, appear to seek riskier options, while the length of the temporal distance is associated with increasing propensity toward risky decision making. The impact of temporal distance on police misconduct was investigated utilising a machine learning analysis performed on a large police complaints dataset, designed to determine procedural factors which contribute most to serious misconduct among police officers in New South Wales. Results indicated that temporally proximal remediation of complaint matters offered significantly better outcomes for officers who were subject to complaints, while a more distal resolution significantly reduced potential benefit


Biography:

Tim currently leads Professional Standards Research in the NSW Police Force, with a background in substance use and addiction research. His PhD thesis focused on the development and testing of a predictive model for Police misconduct and deviance, resulting in a functional early intervention model.

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