Women’s Movement and Change of Women’s Status in China

Introduction: The year 1999 marks the fiftieth anniversary of the People’s Republic of China. Achievement of women’s liberation has been on the agenda of the Chinese Communist Party since the beginning of the PRC. This research studies the women’s movement and changes in women’s status in China. The first part of the paper reviews the history of the women’s movement in China and relevant theoretical perspectives on gender stratification. The second part examines empirical and secondary data to demonstrate the transformations of women’s status in China. The final section examines some conclusions emerging from the data. The findings show that the change of women’s status, measured in terms of income, occupation and education, over the last fifty years has been unstable and inconsistent. Although China has been tremendously successful in achieving gender equality, women still suffer a lower status compared with men

More about Research in Ambiguous, Conflictual, and Changing Contexts : Studying Ethnic Populations in China, Xi\u27an to Urumqi

A recent article in this journal discusses ways to manage uncertainty when the research field abruptly and significantly changes on researchers working inside their own society (Kacen & Chaitin, 2006). Our essay extends this discussion by asking: How do researchers manage ambiguous, conflictual, and rapidly changing events when they engage in study outside their own society? We describe three aspects of our data collection experience that coincidentally began one week before the Urumqi city, Xinjiang, China, riots of 2009 in which over 200 people were reported as killed and several thousand injured: (a). our original research agenda and the uncertain situation in Xinjiang in recent years; (b). how we modified our research project and approach to data collection; and (c). what we learned that can contribute to knowledge about conducting research under ambiguous, potentially unstable and rapidly changing socio-political conditions

The Impact of Online Negative Reviews on Consumer Purchasing Intention in the Apparel Industry: The Mediating Role of Perceived Risk

This paper constructs a model that examines how online negative reviews impact consumer purchasing intention in the apparel industry under the mediating role of perceived risk. A questionnaire survey was conducted among consumers in the clothing industry, and the collected data underwent descriptive statistical analysis, correlation analysis, and regression analysis. The results indicate that the quantity, content quality, and perceived risk of online negative reviews negatively affect purchasing intention, with the inhibitory effect of content quality being particularly prominent. Furthermore, the mediating impact of perceived risk is validated. To better understand the perceptual processes behind consumer behavior, this study reveals this connection and provides targeted online reputation management recommendations for apparel businesses. The managerial implications of this paper lie in the necessity for clothing enterprises to establish effective feedback mechanisms for online negative reviews and implement measures to reduce consumers’ perceived risk in purchasing decisions

Boosting Continuous Control with Consistency Policy

Due to its training stability and strong expression, the diffusion model has attracted considerable attention in offline reinforcement learning. However, several challenges have also come with it: 1) The demand for a large number of diffusion steps makes the diffusion-model-based methods time inefficient and limits their applications in real-time control; 2) How to achieve policy improvement with accurate guidance for diffusion model-based policy is still an open problem. Inspired by the consistency model, we propose a novel time-efficiency method named Consistency Policy with Q-Learning (CPQL), which derives action from noise by a single step. By establishing a mapping from the reverse diffusion trajectories to the desired policy, we simultaneously address the issues of time efficiency and inaccurate guidance when updating diffusion model-based policy with the learned Q-function. We demonstrate that CPQL can achieve policy improvement with accurate guidance for offline reinforcement learning, and can be seamlessly extended for online RL tasks. Experimental results indicate that CPQL achieves new state-of-the-art performance on 11 offline and 21 online tasks, significantly improving inference speed by nearly 45 times compared to Diffusion-QL. We will release our code later.Comment: 18 pages, 9 page

Enhancing a Sense of Competence at Work by Engaging in Proactive Behavior: The Role of Proactive Personality

To understand how individuals’ senses of competence are cultivated, scholars have primarily focused on situational factors such as job autonomy and supervisor support. Against this backdrop, we propose that individuals can work as active agents and enhance their sense of competence by initiating actions that aim to master the environment. We adopt the behavioral concordance model and propose that people higher in proactive personality are more likely to engage in proactive behavior that elevates their senses of competence over time. We further propose that such behavioral concordance contributes to boosting a sense of competence is more prominent among those with higher proactive personality. Our predictions are supported by data from 172 employees and their direct supervisors in China, after controlling for the effect of job autonomy and supervisor support for autonomy. Specifically, only those higher in proactive personality engaged in more proactive behavior and increased their sense of competence over time. This study highlights both a self-initiated and a behavioral perspective on understanding the development of a sense of competence

Artificial bee colony algorithm with time-varying strategy

Artificial bee colony (ABC) is one of the newest additions to the class of swarm intelligence. ABC algorithm has been shown to be competitive with some other population-based algorithms. However, there is still an insufficiency that ABC is good at exploration but poor at exploitation. To make a proper balance between these two conflictive factors, this paper proposed a novel ABC variant with a time-varying strategy where the ratio between the number of employed bees and the number of onlooker bees varies with time. The linear and nonlinear time-varying strategies can be incorporated into the basic ABC algorithm, yielding ABC-LTVS and ABC-NTVS algorithms, respectively. The effects of the added parameters in the two new ABC algorithms are also studied through solving some representative benchmark functions. The proposed ABC algorithm is a simple and easy modification to the structure of the basic ABC algorithm. Moreover, the proposed approach is general and can be incorporated in other ABC variants. A set of 21 benchmark functions in 30 and 50 dimensions are utilized in the experimental studies. The experimental results show the effectiveness of the proposed time-varying strategy