INFORMATION RESEARCH

Tracing the Historical Trajectory of Scientific Integration:AIGC Empowers Interdisciplinarity Measurement Research

  • Liu Jiangfeng ,
  • Zhang Ran ,
  • Wang Dongbo ,
  • Pei Lei
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  • 1 Laboratory of Data Intelligence and Cross Innovation, Nanjing University, Nanjing 210023;
    2 School of Information Management, Nanjing University, Nanjing 210023;
    3 College of Information Management, Nanjing Agricultural University, Nanjing 210095;
    4 Research Center for Humanities and Social Computing, Nanjing Agricultural University, Nanjing 210095

Received date: 2023-08-14

  Revised date: 2023-11-26

  Online published: 2024-04-24

Abstract

[Purpose/Significance] The generative large language model has changed the paradigm of natural language processing research, promoted the new trend of AI-enabled social science research, and provided new ideas for quantitatively calculating disciplinary crossover and integration of humanities and social science disciplines from the perspective of deep semantic features of texts.[Method/Process] In this paper, it used ChatGPT to discriminate the academic literature in humanities and social sciences. Based on few-shot learning, it recognized the discipline in the model prediction, measured the multidisciplinary classification from the perspective of the knowledge decentralized distribution, made a comparative analysis between the results and the corresponding disciplines of the journals. Finally, it put forward the indexes of cross-disciplinary richness, cross-disciplinary closeness, and subjectivity, combined with the interdisciplinary degree, interdisciplinary quantitative research.[Result/Conclusion] This paper focuses on AIGC-enabled interdisciplinarity measurement, proposes a set of research framework methods from the judgment of disciplinary attribution, the extraction of disciplinary names in the answer set of the generative model, the empowerment of multidisciplinary candidate problems, and interdisciplinarity content-based metric indicators, and realizes the AIGC-enabled social sciences research from the content. It provides a reference for the internal logic of social science research.

Cite this article

Liu Jiangfeng , Zhang Ran , Wang Dongbo , Pei Lei . Tracing the Historical Trajectory of Scientific Integration:AIGC Empowers Interdisciplinarity Measurement Research[J]. Library and Information Service, 2024 , 68(7) : 137 -150 . DOI: 10.13266/j.issn.0252-3116.2024.07.013

References

[1] 陈必坤,刘钰馨,白宽,等.基于科学建模的学科交叉测度研究综述[J].图书情报工作, 2022, 66(18):126.(CHEN B K, LIU Y X, BAI K, et al. Literature review of interdisciplinary measurement based on modeling of science[J]. Library and information service, 2022, 66(18):126.)
[2] 张雪,张志强.学科交叉研究系统综述[J].图书情报工作, 2020, 64(14):112-125.(ZHANG X, ZHANG Z Q. Review on interdisciplinary research[J]. Library and information service, 2020, 64(14):112-125.)
[3] 吴小兰,章成志.国家自然科学基金视角下学科跨学科性演变研究[J].科技情报研究, 2022, 4(3):20-32.(WU X L, ZHANG C Z. Interdisciplinarity evolution of disciplines:a perspective of NSFC[J]. Scientific information research, 2022, 4(3):20-32.)
[4] 熊文靓,付慧真.交叉科学测度理论、进展与展望[J].图书情报工作, 2022, 66(21):132-144.(XIONG W L, FU H Z. Theory, research progress and prospect of interdisciplinary research measurement[J]. Library and information service, 2022, 66(21):132-144.)
[5] 曾粤亮,司莉.跨学科科研合作:背景、理论研究与实践进展[J].图书情报工作, 2021, 65(10):127-140.(ZENG Y L, SI L. Interdisciplinary research collaboration:background, theoretical research and practice progress[J]. Library and information service, 2021, 65(10):127-140.)
[6] 杨良斌,周秋菊,金碧辉.基于文献计量的跨学科测度及实证研究[J].图书情报工作, 2009, 53(10):87-90, 115.(YANG L B, ZHOU Q J, JIN B H. The interdisciplinary measure and empirical research based on bibliometrics[J]. Library and information service, 2009, 53(10):87-90, 115.)
[7] 刘浏,王东波.基于论文自动分类的社科类学科跨学科性研究[J].数据分析与知识发现, 2018, 2(3):30-38.(LIU L, WANG D B. Identifying interdisciplinary social science research based on article classification[J]. Data analysis and knowledge discovery, 2018, 2(3):30-38.)
[8] 吕琦,上官燕红,张琳,等.基于文本内容自动分类的跨学科测度研究[J].数据分析与知识发现, 2023, 7(4):56-67.(LV Q, SHNAGGUAN Y H, ZHANG L, et al. Interdisciplinary measurement based on automatic classification of text content[J]. Data analysis and knowledge discovery, 2023, 7(4):56-67.)
[9] 陆伟,刘家伟,马永强,等. ChatGPT为代表的大模型对信息资源管理的影响[J].图书情报知识, 2023, 40(2):6-9, 70.(LU W, LIU J W, MA Q, et al. The influence of large language models represented by ChatGPT on information resources management[J]. Documentation, information & knowledge, 2023, 40(2):6-9, 70.)
[10] PORTER A L, CHUBIN D E. An indicator of cross-disciplinary research[J]. Scientometrics, 1985, 8(3-4):161-176.
[11] STIRLING A. A general framework for analysing diversity in science, technology and society[J]. Journal of the royal society interface, 2007, 4(15):707-719.
[12] L E YD E SD O R F F L, R A F O L S I. Indicators of the interdisciplinarity of journals:diversity, centrality, and citations[J]. Journal of informetrics, 2011, 5(1):87-100.
[13] 陈光华,梁琼方.台湾图书资讯学之跨学科交流[J].图书资讯学刊, 2004, 2(2):31-55(CHENG G H, LIANG Q F. Disciplinary interflow of library and information science in Taiwan[J]. Journal of library and information studies, 2004, 2(2):31-55.)
[14] 杨良斌,金碧辉.跨学科测度指标体系的构建研究[J].情报杂志, 2009, 28(7):65-69.(YANG L B, JIN B H. A study on construction of the measure indices system of interdisciplinarity[J]. Journal of intelligence, 2009, 28(7):65-69.)
[15] 黄颖,高天舒,王志楠,等.基于Web of science分类的跨学科测度研究[J].科研管理, 2016, 37(3):124-132.(HUANG Y, GAO T S, Wang Z N, et al. Interdisciplinary measurement research based on Web of Science classification[J]. Science research management, 2016, 37(3):124-132.)
[16] LEYDESDORFF L. Diversity and interdisciplinarity:how can one distinguish and recombine disparity, variety, and balance?[J]. Scientometrics, 2018, 116(3):2113-2121.
[17] 马瑞敏,闫晓慧,申楠.学科交叉直接测度研究[J].情报学报, 2019, 38(7):688-696.(MA R M, YAN X H, SHEN N. Direct measurement of the degree of interdisciplinarity[J]. Journal of the China Society for Scientific and Technical Information, 2019, 38(7):688-696.)
[18] 姚茹,王晋飞,邬金鸣,等.潜在学科交叉主题识别方法研究[J].图书情报工作, 2023, 67(15):80-93.(YAO R, WANG J F, WU J M, et al. Research on potential interdisciplinary topic identification method[J]. Library and information service, 2023, 67(15):80-93.)
[19] 黄华.基于决策树与SVM融合学习的科技文献分类方法研究[D].郑州:河南工业大学, 2011.(HUANG H. The fusion learning on technical text categorization based on decision tree and SVM[D]. Zhengzhou:Henan University of Technology, 2011.)
[20] 杨敏,谷俊.基于SVM的中文书目自动分类及应用研究[J].图书情报工作, 2012, 56(9):114-119.(YANG M, GU J. Study and apply of Chinese bibliographies automatic classification based on support vector machine[J] Library and information service, 2012, 56(9):114-119.)
[21] 宫小翠,安新颖,单连慧.基于Labeled lda主题模型的医学文献自动分类法[J].中华医学图书情报杂志, 2018, 27(10):53-58.(GONG X C, AN X Y, SHAN L H. Labeled LDA topic model-based automatic classification of medical literature[J]. Chinese journal of medical library and information science, 2018, 27(10):53-58.)
[22] 王清.基于随机森林的图书馆馆藏文献自动分类方法[J].自动化技术与应用, 2022, 41(7):51-53, 72.(WANG Q. Automatic classification of library collections based on random forest[J]. Techniques of automatic and applications, 2022, 41(7):51-53, 72.)
[23] 何力,郑灶贤,项凤涛,等.基于深度学习的文本分类技术研究进展[J].计算机工程, 2021, 47(2):1-11.(HE L, ZHENG D X, XIANG F T, et al. Research progress of text classification technology based on deep learning[J]. Computer engineering, 2021, 47(2):1-11.)
[24] KIM Y. Convolutional neural networks for sentence classification:arXiv:1408.5882[Z/OL]. arXiv, 2014(2014-09-02)[2023-11-21]. http://arxiv.org/abs/1408.5882.
[25] 罗鹏程,王一博,王继民.基于深度预训练语言模型的文献学科自动分类研究[J].情报学报, 2020, 39(10):1046-1059.(LUO P C, WANG Y B, WANG J M. Automatic discipline classification for scientific papers based on a deep pre-training language model[J]. Journal of the China Society for Scientific and Technical Information, 2020, 39(10):1046-1059.)
[26] 段丹丹,唐加山,温勇,等.基于BERT模型的中文短文本分类算法[J].计算机工程, 2021, 47(1):79-86.(DUAN D D, TANG J S, WEN Y, et al. Chinese short text classification algorithm based on BERT model[J]. Computer engineering, 2021, 47(1):79-86.)
[27] 沈自强,李晔,丁青艳,等.基于BERT模型的科技政策文本分类研究[J].数字图书馆论坛, 2022(1):10-16.(SHEN Z Q, LI Y, DING Q Y, et al. Research on science and technology policy text classification based on BERT model[J]. Digital library forum, 2022(1):10-16.)
[28] LU Y, LIU Q, DAI D, et al. Unified structure generation for universal information extraction:arXiv:2203.12277[Z/OL]. arXiv, 2022(2022-03-23)[2024-3-1]. http://arxiv.org/abs/2203.12277.
[29] TRIEBE O, HEWAMALAGE H, PIYUGINA P, et al. Neural prophet:explainable forecasting at scale:arXiv:2111.15397[Z/OL]. arXiv, 2021(2021-11-29)[2024-3-1]. http://arxiv.org/abs/2111.15397.
[30] 郝雯柯,杨建林.基于语义表示和动态主题模型的社科领域新兴主题预测研究[J].情报理论与实践, 2023, 46(2):184-193.(HAO W K, YANG J L. Predicting emerging topics in social sciences based on semantic representation and dynamic topic model[J]. Information studies:theory & application, 2023, 46(2):184-193.)
[31] 刘铭基,田雅楠,张亮,等.基于Prophet-ARIMA模型的民航周转量预测研究[J].计算机技术与发展, 2022, 32(2):148-153, 160.(LIU M J, TIAN Y N, ZHANG Li, et al. Application of Prophet-ARIMA combined model in forecast of civil aviation turnover[J]. Computer technology and development, 2022, 32(2):148-153, 160.)
[32] 张培,阮选敏,吕冬晴,等.人文社会科学学者的跨学科性对被引的影响研究[J].情报学报, 2019, 38(7):675-687.(ZHANG P, RUAN X M, LV D Q, et al. Research on the relationship between the interdisciplinarity of scholars and its impact on citation in the humanities and social sciences[J]. Journal of the China Society for Scientific and Technical Information, 2019, 38(7):675-687.)
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