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손해사정사의 업무범위 확장에 관한 연구 -합의 및 분쟁의 조정 대리권을 중심으로-
최유정 ( Yujeong Choi ),김진형 ( Jinhyung Kim ) 한국손해사정학회 2019 손해사정연구 Vol.19 No.-
1978년 보험업법의 개정으로 국내에 손해사정사제도가 처음 도입된 이후 40여년간 제도적·실무적으로 많은 발전을 이루어 왔다. 손해사정은 보험소비자의 손실에 대한 경제적 보상을 실현시켜주는 수단으로, 손해사정사는 보험계약자의 권익보장과 보험산업 내의 불필요한 분쟁을 최소화하기 위해 자신의 업무에 임한다. 하지만 손해사정사의 업무 영역에 대한 잦은 분쟁으로 인해 손해사정사에 대한 사회적 가치가 훼손되고, 이와 더불어 보험계약자의 보험금 지급 청구에 대한 정당한 권리요구의 기회마저 박탈되고 있다. 손해사정사의 업무영역에 관한 분쟁의 주요 원인은 현재 손해사정사에 대한 명확한 법적 정의가 부재하고, 손해사정 실무를 포괄하지 못하는 규정으로 인해 분쟁소지가 다분하며, 손해사정사의 대리권이 다른 전문자격사들에 비해서도 지나치게 제한되고 있다는 점에 있다. 따라서 본 논문에서는 손해사정사제도의 입법취지를 살리기 위해, 현재 발생하고 있는 업무범위 분쟁을 최소화하면서도, 손해사정사에게 보험금 지급청구나 사정에 관한 합의를 진행할 수 있는 대리권을 부여하고, 관련 분쟁조정의 참여를 통해 전문자격사로서의 업무를 지속할 수 있는 방안에 대해 논의하고자 한다. After the claims adjuster system was first introduced in Korea with the revision of the 1978 Insurance Business Act, there have been many institutional and operational developments over the past 40 years. Claims adjustment is a way to realize economic compensation regarding the loss of insurance consumers and the claims adjuster fulfills their duties to guarantee the rights and interests of policyholders and to minimize unnecessary disputes within the insurance industry. However, frequent disputes regarding the work scope of claims adjuster harm their social value and in addition, policyholders are being deprived of their opportunities to legitimately demand their rights regarding insurance payment requests. The main cause of disputes regarding the work domain of claims adjuster is that there is no clear legal definition regarding claims adjuster, there are many possibilities for dispute due to regulations that do not include the practical affairs of claims adjustment, and the representation rights of claims adjuster is excessively limited compared to those of other professional appraisers. Therefore, to revive the legislative intent of the claims adjuster system, this thesis attempts to discuss methods that will minimize work scope disputes that are currently occurring, offer representation rights to claims adjuster with which they can request payment of insurance and conduct settlements regarding claims, and allow claims adjuster to continue their work as professional appraisers through participation in related dispute mediations.
프로젝트 기반의 한국어 교육 -북트레일러 제작 수업 및 학습자 반응 연구
최유정 ( Choi Yujeong ),류나영 ( Ryu Na-young ) 연세대학교 언어연구교육원 한국어학당 2018 외국어로서의 한국어교육 Vol.49 No.-
The technological advances and ubiquitous use of Internet-access devices in recent years are changing the trajectory of language learning. Combining technology and project-based learning as a teaching method can help students gain knowledge and develop skills that help them solve problems and become responsible and autonomous in their learning. The development and sharing of project-based activities for classroom use is now becoming an important issue. In response to the need, this paper introduces the phases of planning and implementing a book trailer activity as an instruction model as part of the curriculum for use in an advanced Korean language class. A book trailer is a video that introduces a book and used in the classroom can help students exercise language, interaction, and complex thinking skills. Through the project learners highlight the story using their imaginations in a persuasive manner. The paper also discusses the results of implementation of the activity with learner responses on the benefits of the project. The results of the learners' responses suggest that after this activity they better understood the content of the books, felt more confident in reading Korean literature, and were able to understand new vocabulary and grammar in the context. (University of Toronto)
노현정(Hyeonjeong Noh),전주예(Juye Jeon),최유정(Yujeong Choi),하민영(Minyoung Ha),한철(Cheol E. Han) 대한전자공학회 2020 대한전자공학회 학술대회 Vol.2020 No.8
It is now well-proven that wearing the mask is highly effective to prevent disease spread. In this study, we proposed an artificial intelligence-based system to classify whether a person wears a mask or not. We used pretrained convolutional neural network (CNN) to classify whether a person in given images wears a mask or not. The final model achieved 92.28% accuracy. We believe that this system can be used for the entrance of buildings to achieve successful national quarantine.