Fidelity, Remix, and the Adaptive Potential: Leveraging AI and ML Techniques in Literary Adaptation Theory
Fidelity, Remix, and the Adaptive Potential: Leveraging AI and ML Techniques in Literary Adaptation Theory
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211151147
- ISBN
- 9798382715803
- DDC
- 820
- 저자명
- Glass, Grant.
- 서명/저자
- Fidelity, Remix, and the Adaptive Potential: Leveraging AI and ML Techniques in Literary Adaptation Theory
- 발행사항
- [Sl] : The University of North Carolina at Chapel Hill, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 263 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 85-11, Section: B.
- 주기사항
- Advisor: Moskal, Jeanne;Thompson, James .
- 학위논문주기
- Thesis (Ph.D.)--The University of North Carolina at Chapel Hill, 2024.
- 초록/해제
- 요약This dissertation explores the potential applications of artificial intelligence (AI) and machine learning (ML) techniques in the field of literary adaptation and examines the implications of these technologies for questions of originality, intellectual property, and remixing. The study aims to develop a comprehensive framework for applying AI and ML to identify new possibilities for adapting literary works across different media formats and genres. Through a series of case studies focusing on Daniel Defoe's Robinson Crusoe, Mary Shelley's Frankenstein, and Jane Austen's Pride and Prejudice, the dissertation demonstrates the effectiveness of these techniques in uncovering key textual features, narrative patterns, and thematic elements that can inform the adaptation process.By comparing AI and ML-driven approaches with traditional methods, the study highlights the relative strengths and weaknesses of these technologies in terms of efficiency, accuracy, and creativity. The dissertation also critically examines the legal, ethical, and cultural implications of using AI and ML in literary adaptation, contributing to ongoing debates about the changing nature of originality, authorship, and ownership in the digital age. Furthermore, the study explores the potential for collaborative partnerships between human creators and intelligent systems, identifying best practices and strategies for fostering productive collaborations in the creative process.The significance of this research lies in its interdisciplinary approach, bridging the fields of literary studies, digital humanities, and computer science to provide new insights into the complex relationship between technology, creativity, and culture. By developing a robust framework for applying AI and ML to literary adaptation and analyzing the implications of these technologies, the dissertation opens new avenues for research and practice in the digital age. The findings have important implications for the future of creative industries, as well as for ongoing discussions about the role of technology in shaping cultural production and consumption. Ultimately, the dissertation demonstrates the value of critical engagement with AI and ML in the humanities, highlighting the need for collaborative and reflective approaches to harnessing the potential of these technologies while navigating their challenges and limitations.
- 일반주제명
- English literature
- 일반주제명
- Computer science
- 키워드
- Machine learning
- 기타저자
- The University of North Carolina at Chapel Hill English and Comparative Literature
- 기본자료저록
- Dissertations Abstracts International. 85-11B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■020 ▼a9798382715803
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■0820 ▼a820
■1001 ▼aGlass, Grant.
■24510▼aFidelity, Remix, and the Adaptive Potential: Leveraging AI and ML Techniques in Literary Adaptation Theory
■260 ▼a[Sl]▼bThe University of North Carolina at Chapel Hill▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a263 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 85-11, Section: B.
■500 ▼aAdvisor: Moskal, Jeanne;Thompson, James .
■5021 ▼aThesis (Ph.D.)--The University of North Carolina at Chapel Hill, 2024.
■520 ▼aThis dissertation explores the potential applications of artificial intelligence (AI) and machine learning (ML) techniques in the field of literary adaptation and examines the implications of these technologies for questions of originality, intellectual property, and remixing. The study aims to develop a comprehensive framework for applying AI and ML to identify new possibilities for adapting literary works across different media formats and genres. Through a series of case studies focusing on Daniel Defoe's Robinson Crusoe, Mary Shelley's Frankenstein, and Jane Austen's Pride and Prejudice, the dissertation demonstrates the effectiveness of these techniques in uncovering key textual features, narrative patterns, and thematic elements that can inform the adaptation process.By comparing AI and ML-driven approaches with traditional methods, the study highlights the relative strengths and weaknesses of these technologies in terms of efficiency, accuracy, and creativity. The dissertation also critically examines the legal, ethical, and cultural implications of using AI and ML in literary adaptation, contributing to ongoing debates about the changing nature of originality, authorship, and ownership in the digital age. Furthermore, the study explores the potential for collaborative partnerships between human creators and intelligent systems, identifying best practices and strategies for fostering productive collaborations in the creative process.The significance of this research lies in its interdisciplinary approach, bridging the fields of literary studies, digital humanities, and computer science to provide new insights into the complex relationship between technology, creativity, and culture. By developing a robust framework for applying AI and ML to literary adaptation and analyzing the implications of these technologies, the dissertation opens new avenues for research and practice in the digital age. The findings have important implications for the future of creative industries, as well as for ongoing discussions about the role of technology in shaping cultural production and consumption. Ultimately, the dissertation demonstrates the value of critical engagement with AI and ML in the humanities, highlighting the need for collaborative and reflective approaches to harnessing the potential of these technologies while navigating their challenges and limitations.
■590 ▼aSchool code: 0153.
■650 4▼aEnglish literature
■650 4▼aBritish & Irish literature
■650 4▼aComputer science
■653 ▼aLiterary adaptation theory
■653 ▼aMachine learning
■653 ▼aAdaptation process
■653 ▼aNarrative patterns
■653 ▼aCultural production
■690 ▼a0593
■690 ▼a0800
■690 ▼a0984
■71020▼aThe University of North Carolina at Chapel Hill▼bEnglish and Comparative Literature.
■7730 ▼tDissertations Abstracts International▼g85-11B.
■790 ▼a0153
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17160995▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
