Abstract
Previous research has studied effective self-protective behaviors, such as a victim’s physical resistance leading to the avoidance of sexual victimization. However, there are few studies on effective self-protective behavioral sequences, such as an offender’s physical violence followed by the victim’s physical resistance. Our study aims to clarify these sequences through a supervised machine learning approach. The samples consisted of 88 official documents on sexual assaults involving women, committed by male offenders incarcerated in a Japanese local prison. These crimes were classified as completed or attempted cases based on judges’ evaluations. All phrases in each crime description were also partitioned and coded according to the Japanese Penal Code. The support vector machine identified the most likely sequences of behaviors to predict completed and attempted cases. Approximately 90% of cases were correctly predicted through the identification of behavior sequences. The sequence involving an offender’s violence followed by the victim’s physical resistance predicted attempted sexual assault. However, the sequence involving a victim’s general resistance followed by the offender’s violence predicted completed sexual assault. Victims’ and offender’s behaviors need to be interpreted from behavioral sequence perspectives rather than a single action perspective. The supervised machine learning methodologies may extract self-protective behavioral sequences in documents more effectively than other methodologies. The self-protective sequence is a fundamental part of resistance during sexual assault. Training focused on protective sequence contributes to the improvement of resistance training and rape avoidance rates.
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Funding
The present study was funded by a grant from the Foundation for the Fusion of Science and Technologies (Heisei27-10).
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The author declares that he has no conflict of interest.
Ethical Approval
All procedures performed in the present study involving human participants were in accordance with the ethical standards of the institutional research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.
Informed Consent
The present study abbreviated informed consent for three reasons. First, the participants’ informed consent and researchers’ will do not affect our sampling methods, because our criminal suit documents are based on daily activity logs in Japanese courts. Regardless of the participants and researchers’ will, Japanese courts created and stored the documents as their professional tasks. Second, if we analyzed only those who could provide informed consent in prison, the data could be strongly biased and would not be representative of sexual offenders in the Japanese prison. Third, an analysis of criminal documents is the best method to clarify effective behavioral sequences for avoiding rape. The effective behavioral sequences for avoiding rape were essential to prevent sexual victimization.
Given these reasons, we abbreviated informed consent. Abbreviation of informed consent is frequent in epidemiological study (e.g., information about influenza and the Ebola virus was frequently used without informed consent from patients). The present study was also acknowledged by an ethical committee in a local university and a research committee in a local prison in Japan.
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Yokotani, K. Supervised Machine Learning Approach Discovers Protective Sequence for Avoiding Sexual Victimization in Criminal Suit Documents. Asian J Criminol 13, 329–346 (2018). https://doi.org/10.1007/s11417-018-9273-1
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DOI: https://doi.org/10.1007/s11417-018-9273-1