"The Intimate Partner Violence Criminal Career:Predicting Escalation Patterns and Turning Points Across Seven Million People Using AI

本研究整合了亲密伴侣暴力（IPV）和犯罪职业生涯的研究，探索IPV现象中的两个犯罪职业生涯概念的应用：升级和转折点。具体来说，这项研究有助于现有的奖学金，通过确定（1）的因素预测的“转折点”到IPV犯下和（2）的升级模式的IPV的二元体，并提出的因素是预测未来的升级模式。特别是，它是有用的，调查升级模式，因为这些模式之间的表现出的区别可能反映了IPV罪犯和受害者之间的异质性，从而可以支持理论和应用模型的发展，以占和防止IPV的因素，有助于这种异质性。最后，这项研究对大数据的使用有助于刑事司法实践中快速出现的趋势，对如何监管IPV具有重要和新颖的影响。正如Boyd和Crawford（2012年，第663页）所指出的，大数据的定义是其“搜索，聚合和交叉引用大型数据集的能力”，为最终用户提供价值，本研究达到了目标。

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.