人工智能大模型对档案学基础理论的挑战及其回应

doi:10.13366/j.dik.2025.01.057

图书情报知识 ›› 2025, Vol. 42 ›› Issue (1): 57-69.doi: 10.13366/j.dik.2025.01.057

• 学术聚焦(1)·大模型与信息资源管理 • 上一篇下一篇

人工智能大模型对档案学基础理论的挑战及其回应

徐拥军^1,2，陈晓婷¹，闫静³

1.中国人民大学信息资源管理学院，北京，100871；
2.中国人民大学档案事业发展研究中心，北京，100871；
3.山东大学历史学院，济南，250100

出版日期:2025-01-10 发布日期:2025-03-19
通讯作者: 陈晓婷（ORCID: 0000-0001-8806-1606），博士研究生，研究方向：档案与人工智能，Email: cxt@ruc.edu.cn
作者简介:徐拥军（ORCID: 0000-0002-1180-7358），博士，教授，研究方向：档案学基础理论，Email: xyj@ruc.edu.cn；闫静（ORCID: 0000-0003-2436-0804），博士，副教授，研究方向：历史与档案、后现代档案学理论，Email: jingyan@sdu.edu.cn。
基金资助:
本文系中国人民大学科学研究基金项目（中央高校基本科研业务费专项资金资助）“中国自主的档案学知识体系建构”（24XNN012）的研究成果之一。

Challenges of Artificial Intelligence Large Models to the Basic Theory of Archival Science and Its Response

XU Yongjun^1,2，CHEN Xiaoting¹，YAN Jing³

1. School of Information Resources Management, Renmin University of China,Beijing,100871;
2. Archival Undertaking Development Research Center, Renmin University of China, Beijing, 100871;
3. School of History, Shandong University, Jinan, 250100

Online:2025-01-10 Published:2025-03-19
Contact: Correspondence should be addressed to CHEN Xiaoting, Email: cxt@ruc.edu.cn, ORCID: 0000-0001-8806-1606
Supported by:
This is an outcome of the Scientific Research Foundation Project of Renmin University of China "Constructing China's Independent Archival Knowledge System"（24XNN012）supported by Fundamental Research Funds for the Central Universities.

摘要/Abstract

摘要： [目的/意义]人工智能大模型对档案学基础理论带来挑战和变革，亟需探讨其如何推动档案学基础理论的创新与发展。[研究设计/方法]通过对比传统时代和人工智能时代档案学基础理论的内容要点，阐述档案学基础理论对大模型挑战的回应，进一步解释“大模型如何影响档案学基础理论？而档案学基础理论又应当如何回应大模型发起的挑战？”[结论/发现]大模型对档案学基础理论提出了来源原则概念狭窄，文件生命周期延伸、档案价值转化，档案鉴定理论缺失可信性、销而不毁，档案记忆观的建构主体危机等挑战。为回应这些挑战，档案学基础理论需进行自我调适和创新：新来源观概念与背景的再发现，强调文件生命周期的动态连续性，关注档案鉴定理论的可信度检测与被遗忘权，规范算法权力和掌握记忆话语权。[创新/价值]从理论层面重新审视大模型对档案学基础理论的挑战，以及档案学基础理论应如何进行自我修正以做出恰当回应，切实为人工智能时代的档案工作提供理论指导。

关键词: 人工智能, 大模型, 档案学基础理论, 档案工作

Abstract: [Purpose/Significance] The large models of artificial intelligence pose challenges and transformations to the basic theories of archival science, and it is urgent to explore how the large models can drive the innovation and advancement in the basic theories of archival science. [Design/Methodology] By comparing the key points of the basic theories of archival science in the traditional era and the era of artificial intelligence, this paper elaborates the response of the basic theories of archival science to the challenges of the large models, and further explains the question "How do the large models of artificial intelligence affect the basic theories of archival science? And how should the basic theories of archival science respond to the challenge posed by the large models of artificial intelligence?" [Findings/Conclusion] The large models of artificial intelligence have brought challenges to the basic theories of archival science, such as the narrow concept of the principle of provenance, the extension of the records life cycle theory, the transformation of archival value, the lack of credibility in archival appraisal theory, the pin but not destroy, and the crisis of the subject of the construction of the archival memory concept. In response to these challenges, the basic theories of archival science need to undergo self-adjustment and innovation: rediscover the concepts and contexts of the new provenance perspective, emphasize the dynamic continuity of the records life cycle, focus on the credibility assessment in archival appraisal theory and the right to be forgotten, regulate the power of algorithmic, and assert control over the discourse of memory. [Originality/Value] From a theoretical perspective, this article re-examines the challenges posed by large models to the basic theories of archival science, and how the basic theories of archival science should self-correct to respond appropriately, so as to provide theoretical guidance for archival work in the era of artificial intelligence.

Keywords: Artificial intelligence, Large models, Basic theory of archival science, Archival work

徐拥军, 陈晓婷, 闫静. 人工智能大模型对档案学基础理论的挑战及其回应[J]. 图书情报知识, 2025, 42(1): 57-69.

XU Yongjun, CHEN Xiaoting , YAN Jing. Challenges of Artificial Intelligence Large Models to the Basic Theory of Archival Science and Its Response[J]. Documentation, Informaiton & Knowledge, 2025, 42(1): 57-69.

[1]	顾东晓, 赵旺, 王晓玉, 汤志伟, 朱凯旋. 大模型“涌现”视域下的智能健康医疗知识管理体系变革与创新发展[J]. 图书情报知识, 2025, 42(1): 44-56.
[2]	张洋, 吴婷婷, 侯剑华. 大模型驱动科技创新评价若干问题的思考[J]. 图书情报知识, 2025, 42(1): 70-77,88.
[3]	魏远山. 生成式人工智能训练数据的著作权法因应：确需设置合理使用规则吗？[J]. 图书情报知识, 2025, 42(1): 78-88.
[4]	戴文怡, 肖冬梅. 生成式人工智能训练数据的著作权法因应：著作权合规方案[J]. 图书情报知识, 2025, 42(1): 89-100.
[5]	吴欣雨, 吴振新. 人工智能在数字资源长期保存领域应用进展述评[J]. 图书情报知识, 2025, 42(1): 146-157.
[6]	王曦. 人工智能赋能智慧图书馆发展的作用机制[J]. 图书情报知识, 2024, 41(6): 94-101,165.
[7]	张奎, 王秀伟. 生成式AI在传统文化传播中的媒介呈现与风险治理[J]. 图书情报知识, 2024, 41(4): 98-109.
[8]	何静, 沈阳. 基于职业替代概率模型的AIGC职业发展探究[J]. 图书情报知识, 2024, 41(4): 34-41.
[9]	冯昌扬, 陈静怡, 高鹏钰, 曾江峰. 人工智能是否达到奇点——来自图情档职业被人工智能完全替代概率的数据分析与思考[J]. 图书情报知识, 2024, 41(4): 42-56,81.
[10]	周琼, 徐亚苹, 蔡迎春. 高校学生人工智能素养能力现状及影响因素多维分析[J]. 图书情报知识, 2024, 41(3): 38-48.
[11]	唐旭丽, 李信, 易明. 因果推断视角下科研合作多样性和文献新颖性间关系研究[J]. 图书情报知识, 2024, 41(3): 116-129.
[12]	张静蓓, 虞晨琳, 蔡迎春. 人工智能素养教育：全球进展与展望[J]. 图书情报知识, 2024, 41(3): 15-26.
[13]	黄如花, 石乐怡, 吴应强, 陈添. 全球视野下我国人工智能素养教育内容框架的构建[J]. 图书情报知识, 2024, 41(3): 27-37.
[14]	邓胜利, 汪璠, 王浩伟. 在线社区中人工智能生成内容的识别方法研究[J]. 图书情报知识, 2024, 41(2): 28-38,149.
[15]	张春春, 孙瑞英. 如何走出AIGC的“科林格里奇困境”：全流程动态数据合规治理[J]. 图书情报知识, 2024, 41(2): 39-49,66.

人工智能大模型对档案学基础理论的挑战及其回应

Challenges of Artificial Intelligence Large Models to the Basic Theory of Archival Science and Its Response

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价