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Background: Considerable research has shown that ethnic trust reflects the existence of friendly relations among all ethnic groups and ethnic individuals, and can help in resolving ethnic conflicts and contradictions, promoting exchanges among various ethnic groups, which is highly relevant to social stability. Methods: This research, including three studies, aimed to explore the conceptual structure of ethnic trust in China, and develop and validate a measurement of the ethnic trust scale. In the first study, we used free association and indepth interview methodology, applied cluster analysis, multidimensional scaling analysis, and grounded theory to construct the theoretical framework of Chinese people's ethnic trust concept. In the second study, we constructed an initial inventory based on the concept dimensions of ethnic trust established in the first study. We screened items by item analysis and extracted common factors using exploratory factor analysis (EFA), thus determining a total of 48 items in the two subscales (interpersonal-oriented ethnic trust subscale and the intergroup-oriented subscale), which consisted of two dimensions including particular trust and universal trust. In the third study, we used first-and secondorder confirmatory factor analysis (CFA) to test the scale's construct validity. Results: The results indicated a good fit between the two-factor model and the data. And the ethnic trust scale showed very good internal consistency (Cronbach alpha >0.89) and test-retest reliability >0.70. Discussion: Based on our results, have formed a ethnic trust scale by keeping 48 items, which can beused to measure the levels of interpersonal-oriented and group-oriented ethnic trust within the Chinese cultural context. [ABSTRACT FROM AUTHOR]

This paper surveys the extant literature on machine learning, artificial intelligence, and deep learning mechanisms within the financial sphere using bibliometric methods. We considered the conceptual and social structure of publications in ML, AI, and DL in finance to better understand the research's status, development, and growth. The study finds an upsurge in publication trends within this research arena, with a bit of concentration around the financial domain. The institutional contributions from USA and China constitute much of the literature on applying ML and AI in finance. Our analysis identifies emerging research themes, with the most futuristic being ESG scoring using ML and AI. However, we find there is a lack of empirical academic research with a critical appraisal of these algorithmic-based advanced automated financial technologies. There are severe pitfalls in the prediction process using ML and AI due to algorithmic biases, mostly in the areas of insurance, credit scoring and mortgages. Thus, this study indicates the next evolution of ML and DL archetypes in the economic sphere and the need for a strategic turnaround in academics regarding these forces of disruption and innovation that are shaping the future of finance. [ABSTRACT FROM AUTHOR]

The salience of social risks and the incidence of various crises in China have induced widespread concerns among urban residents. Encountering frequent risks places higher demands on the cognition of urban residents. The concept of safety cognition capability is defined within the context of urban residents' daily life, and measurement instruments are developed and tested to lay the foundation for grasping the current safety cognition capability of urban residents and conducting further research. In this study, the five-dimensional structure of urban residents' safety cognition capability (URSCC) was proposed by using the grounded theory method to sort out the interview transcript of interviews with 30 urban residents, and a 38-item URSCC scale was designed and used for surveys conducted in China. The results show that the scale can be used as a valid tool to measure the URSCC, and it can help city managers to better understand the safety needs of residents, as well as monitor the effectiveness of policy implementation. [ABSTRACT FROM AUTHOR]