• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.8 no.4
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• pp.95-109
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• 2013

The practices and processes of HRM (Human Resource Management) for university faculty in Korea depend heavily on assessment of research and teaching rather than the UIC (University-Industry Cooperation) performance. In this regard, HRM of Korean universities is said to be far distant from UIC. Although policy initiatives by the Korean government, notably the MoE (Ministry of Education) have implemented in most universities, the desirable level of UIC could not be achieved yet. Moreover, the very notion of 'university' in Korea is much more to do with 'pure' education and research institution than with 'applied' and 'vocational' purpose. Considering upon HRM practices and organizational culture, for enhancing UIC in Korea, the government's policy should be linked to alter deep-rooted university culture. So the aims of the research are to describe the current state of HRM in Korean and foreign universities; to find out the critical factors of UIC in Korean universities; to analyze the gaps between university research and industrial commercialization based on a conceptual framework, the 'valley of the death'; and to recommend HRM policies fostering UIC for the MoE. For achieving these objectives, we deploy multiple methodologies, namely, in-depth interview, literature survey, and statistical data analysis with regard to UIC. Analyzing the data we have collected, the present research sheds light on all aspects of HRM processes and UICs. And the main policy implication is restricted to the Korean universities, even if we have collected and analyzed foreign universities, notably universities in the USA. The research findings are mainly two folds. Firstly, the HRM practices among Korean universities are very similar due to the legally institutionalized framework and the government's regulations. Secondly, the difficulties of UIC can be explained by notion of the 'valley of death' ways in which both parties of university and industry are looking for different purposes and directions. In order to overcome the gap in the valley of death, the HRM policy is better to be considered as leverage. Finally, the policy recommendations are as follows. Firstly, various kinds of UIC programs are able to enhance the performances of not only UIC, but also education and research outcome. Secondly, fostering organizational climate and culture for UIC, employing various UIC programs, and hiring industry-experienced faculty are all very important for enhancing the high performance of university. We recommend the HRM policies fostering UIC by means of indirect way rather than funding directly for university. The HRM policy of indirect support is more likely to have long-term effectiveness while the government's direct intervention to UIC will have likely short-term effectiveness as the previous policy initiatives have shown. The MEST's policy means of indirect support might vary from financial incentives to the universities practicing HRM for UIC voluntarily, to information disclosure for UIC. The benefits of the present research can be found in suggesting HRM policy for UIC, highlighting the significance of industry-experienced faculty for UIC, and providing statistical analysis and evidences of UIC in Korean universities.

• Journal of the Korean Society of Child Welfare
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• no.59
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• pp.209-234
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• 2017

This study viewed schools as a cause of students dropping out and posited that dropping out of high school would vary depending on the characteristics and influencing factors of the school from which students were dropping out. Therefore, focusing on schools, we longitudinally investigated the change patterns of school dropout across high schools in the country, and the types of changes in dropping out of high school. In addition, we predicted the general characteristics of schools according to the type of school students were dropping out from, looked at the changes in the major factors (i.e., school violence and school counseling) affecting school dropout, and reviewed schools' long-term efforts and outcomes in relation to school dropout. For this purpose, KERIS EDSS's "Secondary School Information Disclosure Data" were used. The final model included data collected five years20122016) from high schools across the country. The results were as follows. First, in order to examine the longitudinal change patterns of dropping out of high schools, a latent growth models analysis was conducted, and it revealed that, as time passed, the dropout rate decreased. Second, growth mixture modeling was used to explore types according to the change patterns of the school students were dropping out from. The results showed three types: the "remaining in school" type, the "gradually decreasing school dropout" type, and the "increasing school dropping out". Third, the multinomial logistic regression was conducted to predict the general characteristics of schools by type. The results showed that public schools, vocational schools, and schools with a large number of students who have below the basic levels in Korean, English and mathematics were more likely to belong to the "increasing school dropout" type. Further, the larger the total number of students, the higher the probability of belonging to the "remaining in school" type or the "gradually decreasing school dropout" type. Lastly, growth mixture modeling was used to analyze the trend of school violence and school counseling according to the three types. The focus was on the "gradually decreasing school dropout" type. In the case of the "gradually decreasing school dropout" type, it was found that as time passed, the number of school violence cases and the number of offenders gradually decreased. In addition, in terms of change in school counseling the results revealed that the number of placement of professional counselors in schools increased every year and peer counseling was continuously promoted, which may account for the "gradually decreasing school dropout" type.

• Journal of the Korean Institute of Landscape Architecture
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• v.50 no.6
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• pp.110-123
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• 2022

Namsan Park in Seoul was designated as a "grand park" in 1954 and is currently operated as an 'Urban Nature Park Area' and four 'neighborhood parks.' However, despite the park's historical and cultural value as an urban park, it has been discussed mainly from a perspective revolving around notions of a mountain or a city wall. To ensure a comprehensive exploration of Namsan Park's history, this study examined public records at the Seoul Metropolitan Archives (SMA), which houses the city's permanent records for preservation and organization. To this end, documents in the SMA Database (DB) were analyzed, yielding 1,359 records concerning Namsan Park. Based on the contents, general characteristics of the urban park were identified through production periods, record types, and disclosure types. Then, essential keywords concerning organizations, people, geographical areas, subjects, and business functions were examined. Finally, the contents and characteristics of Namsan Park in public records were scrutinized, focusing on specific spaces. This research also uncovered important information, such as park drawings, photos, planting lists, plant parcel lists, and significant discussions and decisions regarding the operation and management of the park. Although the public records do not contain a comprehensive history of Namsan Park, it was possible to check the primary historical changes and deliberation processes pertaining to the park's history. Therefore, continuous research intended to interpret and describe public records is expected to identify many implications. In addition, because the public records showed heterogeneous characteristics that center on specific periods and events, an essential task is to advance collaboration and networking with various related institutions, designers, researchers, and citizens.

• Korean Journal of Social Welfare Studies
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• v.41 no.1
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• pp.251-279
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• 2010

In 2009 the Ministry of Health and Society reported a new milestone in longevity among people living with HIV and AIDS (PLWHA): An individual was reported to be living and healthy after 24 years with HIV/AIDS. Today, PLWHA who receive treatment are more likely to die as a result of cancer or cardiovascular diseases than HIV/AIDS. However, in Korea the public association between HIV/AIDS and death remains strong and PLWHA live with the feeling of being discarded. While great advances have been made in the treatment of HIV/AIDS, understanding of life with HIV/AIDS is just beginning. This study describes the life experiences of PLWHA after being diagnosed with HIV/AIDS. Phenomenological methods were used to analyze the transcripts of semi-structured interviews with six PLWHA. Time is a constant factor in the life experiences of PLWHA. After being diagnosed, participants were shocked, feeling as though the world was caving in and they were living with a time bomb. Compulsory disclosure left PLWHA with a feeling of disconnection from the world. Participants were fired from their jobs, resulting in poverty, isolation and a sense that they were simply waiting to die. However, health professionals informed participants that HIV/AIDS is a manageable illness. With time, PLWHA came to understand HIV/AIDS differently. In accepting their HIV infection, PLWHA created a new sense of meaning in their lives. To be honest to their loved ones and true to their own identity, PLWHA worked to "come out." The experience of coming out helped them to accept themselves as they were and understand their own strength. The most important influence on their treatment, and living with HIV/AIDS generally, was obtaining correct information about HIV/AIDS from health professionals. After accepting that they were living with HIV/AIDS, participants were able to look beyond themselves to support those around them, including family members, friends, and others who encouraged them to recognize and feel confident in their own identity.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.83-102
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• 2021

The government recently announced various policies for developing big-data and artificial intelligence fields to provide a great opportunity to the public with respect to disclosure of high-quality data within public institutions. KSURE(Korea Trade Insurance Corporation) is a major public institution for financial policy in Korea, and thus the company is strongly committed to backing export companies with various systems. Nevertheless, there are still fewer cases of realized business model based on big-data analyses. In this situation, this paper aims to develop a new business model which can be applied to an ex-ante prediction for the likelihood of the insurance accident of credit guarantee. We utilize internal data from KSURE which supports export companies in Korea and apply machine learning models. Then, we conduct performance comparison among the predictive models including Logistic Regression, Random Forest, XGBoost, LightGBM, and DNN(Deep Neural Network). For decades, many researchers have tried to find better models which can help to predict bankruptcy since the ex-ante prediction is crucial for corporate managers, investors, creditors, and other stakeholders. The development of the prediction for financial distress or bankruptcy was originated from Smith(1930), Fitzpatrick(1932), or Merwin(1942). One of the most famous models is the Altman's Z-score model(Altman, 1968) which was based on the multiple discriminant analysis. This model is widely used in both research and practice by this time. The author suggests the score model that utilizes five key financial ratios to predict the probability of bankruptcy in the next two years. Ohlson(1980) introduces logit model to complement some limitations of previous models. Furthermore, Elmer and Borowski(1988) develop and examine a rule-based, automated system which conducts the financial analysis of savings and loans. Since the 1980s, researchers in Korea have started to examine analyses on the prediction of financial distress or bankruptcy. Kim(1987) analyzes financial ratios and develops the prediction model. Also, Han et al.(1995, 1996, 1997, 2003, 2005, 2006) construct the prediction model using various techniques including artificial neural network. Yang(1996) introduces multiple discriminant analysis and logit model. Besides, Kim and Kim(2001) utilize artificial neural network techniques for ex-ante prediction of insolvent enterprises. After that, many scholars have been trying to predict financial distress or bankruptcy more precisely based on diverse models such as Random Forest or SVM. One major distinction of our research from the previous research is that we focus on examining the predicted probability of default for each sample case, not only on investigating the classification accuracy of each model for the entire sample. Most predictive models in this paper show that the level of the accuracy of classification is about 70% based on the entire sample. To be specific, LightGBM model shows the highest accuracy of 71.1% and Logit model indicates the lowest accuracy of 69%. However, we confirm that there are open to multiple interpretations. In the context of the business, we have to put more emphasis on efforts to minimize type 2 error which causes more harmful operating losses for the guaranty company. Thus, we also compare the classification accuracy by splitting predicted probability of the default into ten equal intervals. When we examine the classification accuracy for each interval, Logit model has the highest accuracy of 100% for 0~10% of the predicted probability of the default, however, Logit model has a relatively lower accuracy of 61.5% for 90~100% of the predicted probability of the default. On the other hand, Random Forest, XGBoost, LightGBM, and DNN indicate more desirable results since they indicate a higher level of accuracy for both 0~10% and 90~100% of the predicted probability of the default but have a lower level of accuracy around 50% of the predicted probability of the default. When it comes to the distribution of samples for each predicted probability of the default, both LightGBM and XGBoost models have a relatively large number of samples for both 0~10% and 90~100% of the predicted probability of the default. Although Random Forest model has an advantage with regard to the perspective of classification accuracy with small number of cases, LightGBM or XGBoost could become a more desirable model since they classify large number of cases into the two extreme intervals of the predicted probability of the default, even allowing for their relatively low classification accuracy. Considering the importance of type 2 error and total prediction accuracy, XGBoost and DNN show superior performance. Next, Random Forest and LightGBM show good results, but logistic regression shows the worst performance. However, each predictive model has a comparative advantage in terms of various evaluation standards. For instance, Random Forest model shows almost 100% accuracy for samples which are expected to have a high level of the probability of default. Collectively, we can construct more comprehensive ensemble models which contain multiple classification machine learning models and conduct majority voting for maximizing its overall performance.

• The Korean Society of Law and Medicine
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• v.21 no.2
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• pp.105-162
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• 2020

In the pandemic of infectious disease, restrictions of individual liberty have been justified in the name of public health and public interest. In March 2020, the National Assembly of the Republic of Korea passed the revised bill of the 「Infectious Disease Control and Prevention Act.」 The revised bill newly established the legal basis for forced testing and disclosure of the information of confirmed cases, and also raised the penalties for violation of self-isolation and treatment refusal. This paper examines whether and how these individual liberty limiting clauses be justified, and if so on what ethical and philosophical grounds. The authors propose the theories of the philosophy of law related to the justifiability of liberty-limiting measures by the state and conceptualized the dual-aspect of applying the liberty-limiting principle to the infected patient. In COVID-19 pandemic crisis, the infected person became the 'Patient as Victim and Vector (PVV)' that posits itself on the overlapping area of 'harm to self' and 'harm to others.' In order to apply the liberty-limiting principle proposed by Joel Feinberg to a pandemic with uncertainties, it is necessary to extend the harm principle from 'harm' to 'risk'. Under the crisis with many uncertainties like COVID-19 pandemic, this shift from 'harm' to 'risk' justifies the state's preemptive limitation on individual liberty based on the precautionary principle. This, at the same time, raises concerns of overcriminalization, i.e., too much limitation of individual liberty without sufficient grounds. In this article, we aim to propose principles regarding how to balance between the precautionary principle for preemptive restrictions of liberty and the concerns of overcriminalization. Public health crisis such as the COVID-19 pandemic requires a population approach where the 'population' rather than an 'individual' works as a unit of analysis. We propose the second expansion of the harm principle to be applied to 'population' in order to deal with the public interest and public health. The new concept 'risk to population,' derived from the two arguments stated above, should be introduced to explain the public health crisis like COVID-19 pandemic. We theorize 'the extended harm principle' to include the 'risk to population' as a third liberty-limiting principle following 'harm to others' and 'harm to self.' Lastly, we examine whether the restriction of liberty of the revised 「Infectious Disease Control and Prevention Act」 can be justified under the extended harm principle. First, we conclude that forced isolation of the infected patient could be justified in a pandemic situation by satisfying the 'risk to the population.' Secondly, the forced examination of COVID-19 does not violate the extended harm principle either, based on the high infectivity of asymptomatic infected people to others. Thirdly, however, the provision of forced treatment can not be justified, not only under the traditional harm principle but also under the extended harm principle. Therefore it is necessary to include additional clauses in the provision in order to justify the punishment of treatment refusal even in a pandemic.