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International journal of digital humanities最新文献

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Reproducibility of Indian DH Projects: A case study 印度卫生保健项目的可重复性:一个案例研究
Pub Date : 2023-10-05 DOI: 10.1007/s42803-023-00071-0
Jyothi Justin, Nirmala Menon
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引用次数: 1
Explainability and transparency in the realm of digital humanities: toward a historian XAI 数字人文领域的可解释性和透明性:走向一个历史学家
Pub Date : 2023-10-02 DOI: 10.1007/s42803-023-00070-1
Hassan El-Hajj, Oliver Eberle, Anika Merklein, Anna Siebold, Noga Shlomi, Jochen Büttner, Julius Martinetz, Klaus-Robert Müller, Grégoire Montavon, Matteo Valleriani
Abstract The recent advancements in the field of Artificial Intelligence (AI) translated to an increased adoption of AI technology in the humanities, which is often challenged by the limited amount of annotated data, as well as its heterogeneity. Despite the scarcity of data it has become common practice to design increasingly complex AI models, usually at the expense of human readability, explainability, and trust. This in turn has led to an increased need for tools to help humanities scholars better explain and validate their models as well as their hypotheses. In this paper, we discuss the importance of employing Explainable AI (XAI) methods within the humanities to gain insights into historical processes as well as ensure model reproducibility and a trustworthy scientific result. To drive our point, we present several representative case studies from the Sphaera project where we analyze a large, well-curated corpus of early modern textbooks using an AI model, and rely on the XAI explanatory outputs to generate historical insights concerning their visual content. More specifically, we show that XAI can be used as a partner when investigating debated subjects in the history of science, such as what strategies were used in the early modern period to showcase mathematical instruments and machines.
人工智能(AI)领域的最新进展转化为人工智能技术在人文学科中的应用越来越多,这往往受到注释数据数量有限及其异质性的挑战。尽管数据稀缺,但设计越来越复杂的人工智能模型已经成为一种普遍做法,通常以牺牲人类的可读性、可解释性和信任为代价。这反过来又导致对工具的需求增加,以帮助人文学者更好地解释和验证他们的模型以及他们的假设。在本文中,我们讨论了在人文学科中使用可解释人工智能(XAI)方法的重要性,以深入了解历史进程,并确保模型的可重复性和值得信赖的科学结果。为了推动我们的观点,我们提出了来自Sphaera项目的几个代表性案例研究,我们使用人工智能模型分析了一个大型的、精心策划的早期现代教科书语料库,并依靠XAI解释性输出来生成有关其视觉内容的历史见解。更具体地说,我们表明,在调查科学史上有争议的主题时,可以使用XAI作为合作伙伴,例如在现代早期使用什么策略来展示数学工具和机器。
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引用次数: 2
Measuring digital humanities learning requirements in Spanish & English-speaking practitioner communities 测量西班牙语和英语从业者社区的数字人文学习要求
Pub Date : 2023-09-04 DOI: 10.1007/s42803-023-00066-x
Adam Crymble, Charlotte M. H. Im
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引用次数: 0
Reproducibility and non-traditional authorship attribution: Invitatio ad Arma 再现性和非传统作者归属:邀请与武装
Pub Date : 2023-08-31 DOI: 10.1007/s42803-023-00067-w
J. Rudman
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引用次数: 1
Reproducibility, verifiability, and computational historical research 再现性、可验证性和计算历史研究
Pub Date : 2023-08-31 DOI: 10.1007/s42803-023-00068-9
Toby Burrows
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引用次数: 1
Adjectives and adverbs as stylometric analysis parameters 形容词和副词作为文体分析参数
Pub Date : 2023-05-22 DOI: 10.1007/s42803-023-00065-y
Eugenia Lukin, James Cooper Roberts, David Berdik, Eliana Mugar, P. Juola
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引用次数: 1
Special issue on digital humanities and East Asian studies 数字人文与东亚研究特刊
Pub Date : 2023-02-01 DOI: 10.1007/s42803-022-00064-5
Alíz Horváth, Hilde De Weerdt
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引用次数: 0
An interface to view collections of visual art 查看视觉艺术收藏的界面
Pub Date : 2023-01-27 DOI: 10.1007/s42803-022-00061-8
Hermann Pflüger, Gabriel Viehhauser, Rostislav Tumanov
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引用次数: 0
DH in Japanese studies, Japanese studies in DH: Recent trends, tools, and concepts 日本研究的博士,日本研究的博士:最近的趋势,工具和概念
Pub Date : 2023-01-24 DOI: 10.1007/s42803-022-00063-6
Alíz Horváth
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引用次数: 0
Digital Approaches to Understanding Dongba Pictographs 理解东巴象形文字的数字方法
Pub Date : 2023-01-16 DOI: 10.1007/s42803-022-00057-4
Duoduo Xu
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引用次数: 1
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International journal of digital humanities
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