直近一年間の累計
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ID 114296
タイトル別表記
MACHINE LEARNING APPROACH FOR AVOIDING RAPE
著者
横谷, 謙次 Niigata Prison|Niigata Seiryo University 徳島大学 教育研究者総覧
キーワード
Criminal suit documents
Supervised machine learning
Binary classification
Protective action
Rape
Sexual coercion
資料タイプ
学術雑誌論文
抄録
Effective self-protective behaviors, such as victim's physical resistance for avoiding sexual victimization have been studied. However, effective self-protective behavioral sequences, such as offender's physical violence followed by victim's physical resistance, have not been studied often. Our study aims to clarify these sequences through supervised machine learning approach. The samples consisted of 88 official documents on sexual crimes regarding women committed by male offenders incarcerated in a Japanese local prison. The crimes were classified as completed or attempted cases based on judges’ evaluation. All phrases in each crime description were also partitioned and coded according to the Japanese Penal Code. The Support Vector Machine learned the most likely sequences of behaviors to predict completed and attempted cases. Around 90% of cases were correctly predicted through the identification of sequences of behaviors. The sequence involving the offender’s violence followed by victim’s physical resistance predicted attempted sexual crime. However, the sequence involving victim’s general resistance followed by the offender’s violence predicted completed sexual crime. Timing of victim’s resistance and offender’s violence could affect potential avoidance of sexual victimization.
掲載誌名
Asian Journal of Criminology
ISSN
18710131
1871014X
cat書誌ID
AA12248315
出版者
Springer Nature
13
4
開始ページ
329
終了ページ
346
発行日
2018-07-27
備考
This is a post-peer-review, pre-copyedit version of an article published in Asian Journal of Criminology. The final authenticated version is available online at: https://doi.org/10.1007/s11417-018-9273-1.
EDB ID
出版社版DOI
出版社版URL
フルテキストファイル
言語
eng
著者版フラグ
著者版
部局
総合科学系