메뉴 건너뛰기
.. 내서재 .. 알림
소속 기관/학교 인증
인증하면 논문, 학술자료 등을  무료로 열람할 수 있어요.
한국대학교, 누리자동차, 시립도서관 등 나의 기관을 확인해보세요
(국내 대학 90% 이상 구독 중)
로그인 회원가입 고객센터 ENG
주제분류

추천
검색

논문 기본 정보

자료유형
학술저널
저자정보
저널정보
한국재산법학회 재산법연구 재산법연구 제23권 제3호
발행연도
2007.1
수록면
305 - 338 (34page)

이용수

표지
📌
연구주제
📖
연구배경
🔬
연구방법
🏆
연구결과
AI에게 요청하기
추천
검색

초록· 키워드

오류제보하기
Seatbelt defense in American Law of Torts Kim, Ming-Dong Since the mid-1960s and continuing today, the use of the seatbelt defense in tort litigation has been a subject of heated controversy. Depending upon the jurisdiction, the seatbelt defense can be asserted for one or more purposes: (1) to mitigate damages('duty of mitigation'), (2) to prove 'contributory negligence', 'comparative negligence', and 'assumption of risk', (3) to show 'misuse'; (4) to show 'proximate cause' of an injury. The defense should not apply to determination of liability, but only to apportionment of damages.The failure to wear a safety belt shall not be considered evidence of duty of mitigation, should be admitted to establish comparative negligence. Therefore plaintiff's recovery will be reduced only to the extent that his own lack of reasonable care contributed to his injury.It is necessary for us to discuss the seatbelt defenses that are available in a product-liability action.The application of these defenses to modern strict product liability litigation initially raised two sets of issues. First, courts had to determine whether a specific defense was applicable to the various theories of strict products liability. Second, whether the contours of the defenses were the same as in the context of negligence, for example, contributory negligence, comparative negligence, assumption of risk, and misuse.The policies underlying strict liability do not undermine the application of negligence to strict tort liability actions. We conclude that a system of comparative negligence should be applied to strict product liability, if established, will reduce but not bar plaintiff's claim.

목차

등록된 정보가 없습니다.

참고문헌 (20)

참고문헌 신청

함께 읽어보면 좋을 논문

논문 유사도에 따라 DBpia 가 추천하는 논문입니다. 함께 보면 좋을 연관 논문을 확인해보세요!

이 논문의 저자 정보

최근 본 자료

전체보기

댓글(0)

0