图书情报知识 ›› 2026, Vol. 43 ›› Issue (2): 26-34.doi: 10.13366/j.dik.2026.02.026

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大模型驱动的哲学社会科学创新评价范式转型与实现路径

陆伟   

  1. 武汉大学信息管理学院,武汉,430072
  • 出版日期:2026-03-10 发布日期:2026-05-21
  • 通讯作者: 陆伟(ORCID: 0000-0002-0929-7416),博士,教授,研究方向:大数据方法与技术、情报智能与创新评价、AI治理与人机协同,Email: weilu@whu.edu.cn。
  • 作者简介:本文的合作完成者为罗卓然(ORCID: 0000-0003-0677-8350),博士,馆员,研究方向:创新评价、文本挖掘,Email:zoraluo@whu.edu.cn。
  • 基金资助:
    本文系国家社会科学基金青年项目“数智驱动的学术成果原创性识别与评价研究”(24CTQ054)研究成果之一。

Paradigm Transformation and Implementation Pathway of LLM-Driven Innovation Evaluation in Philosophy and Social Sciences

LU Wei   

  1. School of Information Management, Wuhan University, Wuhan, 430072
  • Online:2026-03-10 Published:2026-05-21
  • Contact: Correspondence should be addressed to LU Wei, Email: weilu@whu.edu.cn, ORCID: 0000-0002-0929-7416
  • Supported by:
    This is an outcome of the Youth Project "Research on the Identification and Evaluation of Academic Originality Driven by Digital Intelligence"(24CTQ054)supported by National Social Science Foundation of China.

摘要: [目的/意义]面向中国特色哲学社会科学自主知识体系建设,探讨大模型驱动的哲学社会科学创新评价框架,为推动评价转向内容识别与证据支撑提供思路。[研究设计/方法]构建大模型驱动的哲学社会科学创新评价框架,提出由价值引领、问题导向、创新贡献、论证支撑、成果影响构成的五维评价体系,并细化为19项指标;在此基础上,设计以全文本语料和多源数据为基础、以模型分析为支撑、以专家参与为保障的实现路径。[结论/发现]大模型有望提升哲学社会科学创新评价的内容识别与证据支撑能力,但现阶段仍主要发挥辅助性评价作用。[创新/价值]从哲学社会科学创新评价的内在要求出发,构建了大模型驱动的创新评价指标体系及其实现路径,为推进哲学社会科学创新评价改革和完善中国特色哲学社会科学创新评价体系提供参考。

关键词: 哲学社会科学, 创新评价, 大语言模型, 范式转型, 自主知识体系

Abstract: [Purpose/Significance] Oriented toward the construction of an independent knowledge system for philosophy and social sciences with Chinese characteristics, this paper explores an LLM-driven framework for innovation evaluation, aiming to shift evaluation toward content understanding and evidence-based assessment. [Design/Methodology] This paper constructs a LLM-driven innovation evaluation framework for philosophy and social sciences, proposing a five-dimensional evaluation system—value guidance, problem orientation, innovation contribution, argumentative support, and outcome impact—operationalized into nineteen indicators. Based on this, it designs an implementation pathway using full-text data and multi-source information, supported by model-based analysis and expert involvement. [Findings/Conclusion] LLMs hold significant promise for enhancing content recognition and evidence-based support in the evaluation of innovation, but currently function mainly as auxiliary evaluation tool. [Originality/Value] Grounded in the intrinsic requisites of academic innovation assessment for philosophy and social sciences, this paper constructs an LLM-driven evaluation indicator system and its implementation pathway, providing a reference for reforming innovation evaluation and improving the evaluation system of philosophy and social sciences in China.

Keywords: Philosophy and social sciences, Innovation evaluation, Large language models, Paradigm transformation, Independent knowledge system