Che 1-kwŏn. Kodae-kŭndae sasang -- che 2-kwŏn. Hyŏndae sasang.

Uju ŭi chuin ŭn nugu in'ga -- Kwahak ch'ŏrhak iran muŏt in'ga (1993) -- Kwahak kisul ŭi ch'ukpok kwa chaeang (Kwahak, ch'ukpok in'ga chaeang in'ga, 2009) -- Munmyŏng ŭi mirae -- Saengmyŏng -- Hwan'gyŏng kwa hwan'gyŏng ch'ŏrhak -- Saengt'ae.

1. Kwangja chŏngsin e kwanhan kich'ojŏk ihae -- 2. Yuga ŭi kwanggyŏn'gwan -- 3. Toga ŭi kwangŏn kwa kwangja chŏngsin -- 4. Ŭmju munhwa wa Wi-Chin ŭi imt'anjŏk kwang -- 5. Songdae ihakchadŭl ŭi kwangjagwan -- 6. Yangmyŏnghak ŭi kwanggyŏn'gwan -- 7. Chosŏnjo yuhakchadŭl ŭi kwanggyŏn'gwan -- 8. Chosŏnjo yuhakcha ŭi yokki yŏnggwi ihae -- 9. Sŏhwa tot'ongnon kwa kwanggyŏn esŏ chunghaeng ŭro -- 10. Kwanggyŏn mihak ŭi munyejŏk chŏgyong -- 11. Chungguk hoehwa e nat'anan kwanggyŏn mihak -- 12. (...) Chungguk sŏye e nat'anan kwanggyŏn mihak -- 13. Chosŏnjo hoehwa e nat'anan kwanggyŏn mihak -- 14. Chosŏnjo sŏye ŭi kwanggyŏn mihak. (shrink)

1. Tan'gun sinhwa ŭi wŏnhyŏngjŏk segyegwan kwa p'ungnyu chŏngsin -- 2. Wŏnhyo ŭi hwajaeng sasang kwa muaehaeng ŭi silch'ŏn -- 3. Ŭisang ŭi 'Hwaŏm ilsŭng pŏpkyedo' e nat'anan Hwaŏm sasang -- 4. Chinul ŭi Tono chŏmsu wa Chŏnghye ssangsu sasang -- 5. Sambong Chŏng To-jŏn ŭi Pulgyo paech'ŏk ŭi naeyong kwa sŏngkyŏk -- 6. T'oegye Yi Hwang ŭi ch'ŏrhakchŏk ipchang kwa 'Kyŏng' sasang -- 7. Kobong Ki Tae-sŭng ŭi hangmun chŏngsin kwa ch'ŏrhak sasang -- 8. Ugye Sŏng Hon ŭi Tohakchŏk (...) sam kwa hangmun yŏnwŏn -- 9. Yulgok Yi I ŭi Taedong sahoeron kwa ch'ŏrhakchŏk chihyang -- 10. Sŏgye Pak Se-dang ŭi Sirhakchŏk sayu ŭi kich'o -- 11. Namdang Han Wŏn-jin ŭi yŏksa insik kwa Chosŏn chunghwajuŭi -- 12. Sŏngdam Song Hwan-gi ŭi 'T'aegŭk tosŏl' haesŏk ŭi kibon ipchang -- 13. Kanjŏng Yi Nŭng-hwa 'Chosŏn Togyosa' ŭi haksulsachŏk ŭiŭi wa han'gye -- 14. Manhae Han Yong-un i pon chuch'e ŭi munje wa minjok chagyŏl ŭi wŏnch'ik. (shrink)

1. An Tŏng-mun ŭi hangmun chŏngsin kwa ch'ŏse pangsik -- 2. An Tŏng-mun ŭi Samsan Sŏwŏn wisang chŏngnip kwa kŭ ŭimi -- 3. Ha Ik-pŏm ŭi sam kwa munhak -- 4. Pak Ch'i-bok ŭi Nammyŏnghak kyesŭng yangsang -- 5. Kang Pyŏng-ju ŭi hangmun kwa munhak -- 6. Chŏng Chae-gyu ŭi hangmun chŏngsin kwa 'Taehak' haesŏk -- 7. Kim Chin-ho ŭi haksŏl e taehayŏ -- 8. Kwak Chong-sŏk ŭi Myŏngdŏksŏl nonjaeng -- 9. Kim Hwang ŭi Kyŏnghak Sipto e taehayŏ.

2. Saebyŏngnyŏk chʻodang esŏ on pʻyŏnji.

This paper documents that business ethics has positive impacts upon the development of intellectual capital. Knowledge has become the most important asset of modern businesses, and this study argues that business ethics is associated with the development of intangible knowledge resources—intellectual capital. Businesses with ethical values at the core reinforce ethical conducts and successfully build trust with their various stakeholders, leading to the formation of an ethical and trustworthy corporate culture and a positive corporate environment. Thus, in this reasoning, an (...) ethical approach to business can encourage open communication, problems solving, knowledge sharing and creativity among employees to increase organisational capital; enhance interactions and relationships with suppliers, customers and other stakeholders to increase social capital; attract and retain good talent to increase human capital. Questionnaire survey is adopted as the research method with businesses in the electronic and information technology industries in Taiwan as sample. The results suggest that business ethics is associated with increased intellectual capital. Thus, this study demonstrates that the development of intellectual capital is in line with strengthened ethics. It contributes to the literature through combining research on business ethics with intellectual capital theories and extends the extant intellectual capital literature. (shrink)

Algorithms can now identify patterns and correlations in the (big) datasets, and predict outcomes based on those identified patterns and correlations with the use of machine learning techniques and big data, decisions can then be made by algorithms themselves in accordance with the predicted outcomes. Yet, algorithms can inherit questionable values from the datasets and acquire biases in the course of (machine) learning, and automated algorithmic decision-making makes it more difficult for people to see algorithms as biased. While researchers have (...) taken the problem of algorithmic bias seriously, but the current discussion on algorithmic fairness tends to conceptualize ‘fairness’ in algorithmic fairness primarily as a technical issue and attempts to implement pre-existing ideas of ‘fairness’ into algorithms. In this paper, I show that such a view of algorithmic fairness as technical issue is unsatisfactory for the type of problem algorithmic fairness presents. Since decisions on fairness measure and the related techniques for algorithms essentially involve choices between competing values, ‘fairness’ in algorithmic fairness should be conceptualized first and foremost as a political issue, and it should be (re)solved by democratic communication. The aim of this paper, therefore, is to explicitly reconceptualize algorithmic fairness as a political question and suggest the current discussion of algorithmic fairness can be strengthened by adopting the accountability for reasonableness framework. (shrink)

오영환 교수 회갑기념 논문집. 과학과 형이상학의 관 계를 공동주제로 한 논문들이다.