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Fidelity, Remix, and the Adaptive Potential: Leveraging AI and ML Techniques in Literary Adaptation Theory /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Fidelity, Remix, and the Adaptive Potential: Leveraging AI and ML Techniques in Literary Adaptation Theory // Grant Glass.
作者:	Glass, Grant,
面頁冊數:	1 electronic resource (263 pages)
附註:	Source: Dissertations Abstracts International, Volume: 85-11, Section: B.
Contained By:	Dissertations Abstracts International85-11B.
標題:	Computer science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31235077
ISBN:	9798382715803

Fidelity, Remix, and the Adaptive Potential: Leveraging AI and ML Techniques in Literary Adaptation Theory /
Glass, Grant,

Fidelity, Remix, and the Adaptive Potential: Leveraging AI and ML Techniques in Literary Adaptation Theory /Grant Glass. - 1 electronic resource (263 pages)

Source: Dissertations Abstracts International, Volume: 85-11, Section: B.

This 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.

English

ISBN: 9798382715803Subjects--Topical Terms:

573171
Computer science.
Subjects--Index Terms:

Literary adaptation theory

Fidelity, Remix, and the Adaptive Potential: Leveraging AI and ML Techniques in Literary Adaptation Theory /
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520 $a This 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.
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650 4 $a British & Irish literature. $3 1148425
650 4 $a English literature. $3 555406
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653 $a Machine learning
653 $a Adaptation process
653 $a Narrative patterns
653 $a Cultural production
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720 1 $a Thompson, James $e degree supervisor.
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