"The Intimate Partner Violence Criminal Career:Predicting Escalation Patterns and Turning Points Across Seven Million People Using AI
Lead Research Organisation:
University of Oxford
Department Name: Sociology
Abstract
This study integrates Intimate Partner Violence (IPV) and criminal careers research by exploring the application of two criminal career concepts within the phenomenon of IPV: escalation and turning points. Specifically, this study contributes to existing scholarship by ascertaining (1) the factors predictive of the 'turning point' into IPV perpetration and (2) the escalation patterns of IPV dyads, and the presenting factors that are predictive of future escalation patterns. In particular, it is useful to investigate escalation patterns since demonstrated distinctions between these patterns may reflect heterogeneity among IPV offenders and victims.
Examining the factors that contribute to such heterogeneity can thus support the development of theoretical and applied models designed to account for and prevent IPV. Finally, this study's use of big data contributes to a fast-emerging trend within criminal justice practice, with important and novel implications for how IPV is policed. As Boyd and Crawford (2012, pg. 663) argue, big data is defined by its 'capacity to search, aggregate, and cross-reference large data sets' in a way that provides value to the end-user, objectives met by this study.
Examining the factors that contribute to such heterogeneity can thus support the development of theoretical and applied models designed to account for and prevent IPV. Finally, this study's use of big data contributes to a fast-emerging trend within criminal justice practice, with important and novel implications for how IPV is policed. As Boyd and Crawford (2012, pg. 663) argue, big data is defined by its 'capacity to search, aggregate, and cross-reference large data sets' in a way that provides value to the end-user, objectives met by this study.
