Using a sample of 553 married and divorced women in a large city in southern China, this study tested the effects of demographic characteristics, risk behaviors, patriarchal ideology, and personal mentality and skills on women's experience of physical violence, psychological violence, controlling behavior, and sexual abuse. Divorced women were more likely than married women to experience all types of IPV. Risk behaviors were consistently related to IPV incidents, whereas the impact of patriarchal ideology and personal mentality and skills was equivocal. Limitations of the study and implications for future research and policy are discussed.
China, as a traditional patriarchal society, provides an excellent context to examine whether and how increased financial independence of women may influence intimate partner violence. This study examines how financial independence influences Chinese women's victimization experiences of physical violence, psychological violence, controlling behavior, and sexual abuse. Data were collected from 600 married or divorced women aged between 20 and 60, who resided in a large metropolitan area in Southern China. Results indicated that while physical violence is reduced by women's financial independence, other forms of connective IPV against women are suggested as expressions of men's desire to keep financially independent women in place.
This work aims to explore the application of deep learning-based artificial intelligence technology in sentencing, to promote the reform and innovation of the judicial system. First, the concept and the principles of sentencing are introduced, and the deep learning model of intelligent robot in trials is proposed. According to related concepts, the issues that need to be solved in artificial intelligence sentencing based on deep learning are introduced. The deep learning model is integrated into the intelligent robot system, to assist in the sentencing of cases. Finally, an example is adopted to illustrate the feasibility of the intelligent robot under deep learning in legal sentencing. The results show that the general final trial periods for cases of traffic accidents, copyright information, trademark infringement, copyright protection, and theft are 1,049, 796, 663, 847, and 201 days, respectively; while the final trial period under artificial intelligence evaluation based on the restricted Boltzmann deep learning model is 458, 387, 376, 438, and 247 days, respectively. The accuracy of trials is above 92%, showing a high application value. It can be observed that expect theft cases, the final trial period for others cases has been effectively reduced. The intelligent robot assistance under the restricted Boltzmann deep learning model can shorten the trial period of cases. The deep learning intelligent robot has a certain auxiliary role in legal sentencing, and this outcome provides a theoretical basis for the research of artificial intelligence technology in legal sentencing.
Analyzing 1268 stratified random samples from one of the biggest cities in China’s Pearl River Delta with the zero-inflated Poisson model, this study identifies the factors associated with the onset and severity of spousal violence by males and females separately under the social exchange perspective. The results indicate that spousal violence follows gender symmetry in migrant families, and violence against men is mainly reflected in psychological violence. The new pattern of “cradle snatching” makes men fully protected in family relationships. However, women’s education level and economic independence do not represent a protective factor against violence from husbands. Patriarchal cognition is deeply rooted in migrant families, even though the family pattern has been changed and women’s status has improved in China. Young couples should contribute to the family according to their own abilities, and should not make either one feel wronged.
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