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Supervised Machine Learning Approach Discovers Protective Sequence for Avoiding Sexual Victimization in Criminal Suit Documents
Asian Journal of Criminology ( IF 1.778 ) Pub Date : 2018-07-27 , DOI: 10.1007/s11417-018-9273-1
Kenji Yokotani

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.

中文翻译:

有监督的机器学习方法发现了避免刑事诉讼文件中性受害的保护序列

先前的研究已经研究了有效的自我保护行为,例如受害者的身体抵抗导致避免性受害。然而,很少有关于有效的自我保护行为序列的研究,例如罪犯的身体暴力,然后是受害者的身体抵抗。我们的研究旨在通过有监督的机器学习方法来阐明这些序列。样本包括 88 份关于女性性侵犯的官方文件,这些文件由日本当地监狱中的男性罪犯实施。根据法官的评估,这些犯罪被归类为已结案或未遂案。每个犯罪描述中的所有短语也根据日本刑法进行了分区和编码。支持向量机识别出最可能的行为序列来预测已完成和未遂案例。通过识别行为序列,大约 90% 的案例被正确预测。涉及犯罪者的暴力以及受害者的身体抵抗的序列预示着性侵犯未遂。然而,受害者的普遍抵抗和犯罪者的暴力行为预示着性侵犯已经完成。受害者和犯罪者的行为需要从行为序列的角度而不是单一的行动角度来解释。监督机器学习方法可以比其他方法更有效地提取文档中的自我保护行为序列。自我保护序列是性侵犯期间抵抗的基本组成部分。
更新日期:2018-07-27
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