Application and Reflection of Full-text Bibliometric from the Perspective of Knowledge Innovation:A Review of the 2024 Academic Salon on Full-text Bibliometric Analysis

Authors

• Haichen ZHOU, National Science Library （Chengdu）, Chinese Academy of Sciences, Chengdu 610299
• Chengzhi ZHANG, School of Economics and Management, Nanjing University of Science and Technology, Nanjing 210094
• Shuo XU, College of Economics and Management, Beijing University of Technology, Beijing 100124
• Jin MAO, School of Information Management, Wuhan University, Wuhan 430072

Keywords

full-text bibliometric; knowledge innovation; large language models; open science; research integrity

Abstract

[Purpose/significance] In December 2024, the 7th Chengdu Conference on Scientometrics & Evaluation was held in Chengdu. This article was conducted to summarize the salon on Full-text Bibliometric analysis of the forum, and reveal the research status and development trends of full-text bibliometric from the perspective of knowledge innovation, providing inspiration for future.

[Method/process] The article systematically summarizes and analyzes the presentations and discussions from the salon, exploring the theoretical foundation, application scenarios, and discussions of full-text bibliometric from the perspective of knowledge innovation.

[Results/conclusion] Full-text Bibliometric can reveal the micro-processes and underlying mechanisms of knowledge innovation, offering new insights and methods for related research. Its applications in technological evolution and industry-academia integration, scientific data warehouse evaluation, policy text analysis and intelligent computing, as well as entity relationship extraction in academic papers, demonstrate its vast potential in promoting academic research, optimizing policy-making, and driving knowledge innovation. However, the integration of full-text bibliometric analysis with large language models, open science, and research integrity presents numerous challenges.

First Page

82

Last Page

90

Submission Date

March 2025

Revision Date

April 2025

Acceptance Date

April 2025

Publication Date

October 2025

Digital Object Identifier (DOI)

ꎺ 10.19809/j.cnki.kjqbyj.2025.04.008

Recommended Citation

ZHOU, Haichen; ZHANG, Chengzhi; XU, Shuo; and MAO, Jin (2025) "Application and Reflection of Full-text Bibliometric from the Perspective of Knowledge Innovation:A Review of the 2024 Academic Salon on Full-text Bibliometric Analysis," Journal of Scientific Information Research: Vol. 7: Iss. 4, Article 8.
DOI: ꎺ 10.19809/j.cnki.kjqbyj.2025.04.008
Available at: https://eng.kjqbyj.com/journal/vol7/iss4/8

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