This study analyzes the attitudes of core policy stakeholders in South Korea to multiculturalism and immigrant integration policies, as well as the factors affecting such attitudes, and suggests theoretical and policy implications. I conducted a survey of public officials, program operators, and academics. The responses suggest that public officials, program operators, and academics in Korea have more positive expectations for immigrants' societal contributions and less fear of social clashes or conflicts due to increasing immigration, compared to the general public. They prefer the assimilationist model, which means that immigrants fully adopt the Korean culture and language as a matter of integration policy. The study sheds light on the stakeholders' attitudes to multiculturalism and immigration integration policy, how their views differ from the general public, and the causes and policy implications of these differences.

This study aims to analyze the motivations and participation and continuity factors of voluntary activities among retired government employees and, thus, offer theoretical and policy recommendations to encourage and retain their involvement. A total of 337 retired government employees in South Korea were surveyed in this study. First, this study found that the most important motivations for volunteering among retired government employees are other-oriented (altruistic) (67.5%) rather than self-oriented (egoistic) (29.2%). Second, personal difficulties in participating in volunteer activities for retired employees were lack of expertise, health problems, and economic burden. Third, the main characteristics of retired employees participating in volunteer activities were retirees who have volunteering experience while working in the past, are women, are active participants in religious activities, and have a high level of education. Finally, retired employees who continued to volunteer after retirement had the following characteristics: a relatively long post-retirement period, volunteer experience while in office, good economic conditions, self-recognized poor health conditions, and high overall life satisfaction.

With the growing impact of ESG on businesses, research related to renewable energy is receiving great attention. Solar cells are one of them, and accordingly, it can be said that the research value of solar cell patent analysis is very high. Patent documents have high research value. Being able to accurately analyze and classify patent documents can reveal several important technical relationships. It can also describe the business trends in that technology. And when it comes to investment, new industrial solutions will also be inspired and proposed to make important decisions. Therefore, we must carefully analyze patent documents and utilize the value of patents. To solve the solar cell patent classification problem, we propose a keyword extraction method and a deep neural network-based solar cell patent classification method. First, solar cell patents are analyzed for pretreatment. It then uses the KeyBERT algorithm to extract keywords and key phrases from the patent abstract to construct a lexical dictionary. We then build a solar cell patent classification model according to the deep neural network. Finally, we use a deep neural network-based solar cell patent classification model to classify power patents, and the training accuracy is greater than 95%. Also, the validation accuracy is about 87.5%. It can be seen that the deep neural network method can not only realize the classification of complex and difficult solar cell patents, but also have a good classification effect.