Attention-Based Deep Learning Model for Predicting Collaborations Between Different Research Affiliations

It is challenging but important to predict the collaborations between different entities which in academia, for example, would enable finding evaluating trends of scientific research collaboration and the provision of decision support for policy formulation and incentive measures. In this paper, we propose an attention-based Long Short-Term Memory Convolutional Neural Network (LSTM-CNN) model to predict the collaborations between different research affiliations, which takes both the influence of research articles and time (year) relationships into consideration. The experimental results show that the proposed model outperforms the competitive Support Vector Machine (SVM), CNN and LSTM methods. It significantly improves the prediction precision by a minimum of 3.23 percent points and up to 10.80 percent points when compared with the mentioned competitive methods, while in terms of the F1-score, the performance is improved by 13.48, 4.85 and 4.24 percent points, respectively.This work was supported in part by the Humanities and Social Science Research Project of the Ministry of Education in China under Grant 17YJCZH262 and Grant 18YJAZH136, in part by the National Natural Science Foundation of China under Grant 61303167, Grant 61702306, Grant 61433012, Grant U1435215, and Grant 71772107, in part by the Natural Science Foundation of Shandong Province under Grant ZR2018BF013 and Grant ZR2017BF015, in part by the Innovative Research Foundation of Qingdao under Grant 18-2-2-41-jch, in part by the Key Project of Industrial Transformation and Upgrading in China under Grant TC170A5SW, and in part by the Scientific Research Foundation of SDUST for Innovative Team under Grant 2015TDJH102

Guanxi exclusion in rural China: parental involvement and students' college access

This study examines the differential patterns of access to higher education of students from rural areas in transition from a planned to a market economy. In respect to college access, the research argues that market reforms have reproduced the advantages for students from the cadre’s and the professional’s families while simultaneously creating new opportunities for the children of the new arising economic elite. Yet, it has performed less for traditional peasant families whose children still fail to gain access to college in proportions higher than the size of the population. Based on the literature, this research places a special emphasis on how economic and cultural resources become the main influence on rural students? college access. The process dimension -- how families from different social backgrounds within rural society involve themselves in the schooling of their children and how this contributes to inequality of college access within rural society, are investigated. This research unpacks this process by examining the school involvement experiences of parents in Zong, a county located in the province of Anhui. Parental involvement is conceptualized in terms of how economic and cultural resources are converted to social capital as part of family strategies within the increasingly stratified social context of rural China. The research identifies the consequences of activating different types of social networks within family and community, and also between family and school to facilitate this process by gaining advantages in access to college. Household interviews and field notes were used as the main methods of data collection with a range of parents and teachers involved in this ethnographic study. The data analysis suggests that state, schools and teachers provide few formal and routine channels for rural parents to become involved in schooling. This raises the importance of family strategic initiatives to employ interpersonal social networks (guanxi) within family, community and between school and family. Parents from cadres and professional backgrounds are capable of maintaining these social networks that are useful for their children’s chances of entering higher education. Their counterparts from the new economic elites? backgrounds have developed the means to capitalize upon their families economic and cultural resources by converting them into social capital that creates advantages in college access for their children. Peasants, however, rely heavily on teachers and relatives in education and are substantially marginalized from those important interpersonal social networks of capital conversion. Although this research found the structure constrains interpersonal social network of peasant families, it also highlights the agency of parents from different families. For example, in some cases it found, that peasants actively use their kinships to create chances for school involvement to potentially improve the chances of their children’s college access. This research is one of the first empirical studies to inquire about the mechanism of capital conversion in affecting higher education opportunities in the post-socialist era, which will help to re-evaluate the influence of market reforms over rural education system in China.published_or_final_versionEducationDoctoralDoctor of Philosoph