Using machine learning to assess rape reports: "Signaling" words about victims' credibility that predict investigative and prosecutorial outcomes.

MLA

Lovell, Rachel E., et al. “Using Machine Learning to Assess Rape Reports: ‘Signaling’ Words about Victims’ Credibility That Predict Investigative and Prosecutorial Outcomes.” Journal of Criminal Justice, vol. 88, Sept. 2023, p. N.PAG. EBSCOhost, https://doi.org/10.1016/j.jcrimjus.2023.102107.

APA

Lovell, R. E., Klingenstein, J., Du, J., Overman, L., Sabo, D., Ye, X., & Flannery, D. J. (2023). Using machine learning to assess rape reports: “Signaling” words about victims’ credibility that predict investigative and prosecutorial outcomes. Journal of Criminal Justice, 88, N.PAG. https://doi.org/10.1016/j.jcrimjus.2023.102107

Chicago

Lovell, Rachel E., Joanna Klingenstein, Jiaxin Du, Laura Overman, Danielle Sabo, Xinyue Ye, and Daniel J. Flannery. 2023. “Using Machine Learning to Assess Rape Reports: ‘Signaling’ Words about Victims’ Credibility That Predict Investigative and Prosecutorial Outcomes.” Journal of Criminal Justice 88 (September): N.PAG. doi:10.1016/j.jcrimjus.2023.102107.