论大型文献计量数据库中可持续发展目标分类的可执行性

Matteo Ottaviani, Stephan Stahlschmidt
{"title":"论大型文献计量数据库中可持续发展目标分类的可执行性","authors":"Matteo Ottaviani, Stephan Stahlschmidt","doi":"arxiv-2405.03007","DOIUrl":null,"url":null,"abstract":"Large bibliometric databases, such as Web of Science, Scopus, and OpenAlex,\nfacilitate bibliometric analyses, but are performative, affecting the\nvisibility of scientific outputs and the impact measurement of participating\nentities. Recently, these databases have taken up the UN's Sustainable\nDevelopment Goals (SDGs) in their respective classifications, which have been\ncriticised for their diverging nature. This work proposes using the feature of\nlarge language models (LLMs) to learn about the \"data bias\" injected by diverse\nSDG classifications into bibliometric data by exploring five SDGs. We build a\nLLM that is fine-tuned in parallel by the diverse SDG classifications inscribed\ninto the databases' SDG classifications. Our results show high sensitivity in\nmodel architecture, classified publications, fine-tuning process, and natural\nlanguage generation. The wide arbitrariness at different levels raises concerns\nabout using LLM in research practice.","PeriodicalId":501285,"journal":{"name":"arXiv - CS - Digital Libraries","volume":"18 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"On the performativity of SDG classifications in large bibliometric databases\",\"authors\":\"Matteo Ottaviani, Stephan Stahlschmidt\",\"doi\":\"arxiv-2405.03007\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Large bibliometric databases, such as Web of Science, Scopus, and OpenAlex,\\nfacilitate bibliometric analyses, but are performative, affecting the\\nvisibility of scientific outputs and the impact measurement of participating\\nentities. Recently, these databases have taken up the UN's Sustainable\\nDevelopment Goals (SDGs) in their respective classifications, which have been\\ncriticised for their diverging nature. This work proposes using the feature of\\nlarge language models (LLMs) to learn about the \\\"data bias\\\" injected by diverse\\nSDG classifications into bibliometric data by exploring five SDGs. We build a\\nLLM that is fine-tuned in parallel by the diverse SDG classifications inscribed\\ninto the databases' SDG classifications. Our results show high sensitivity in\\nmodel architecture, classified publications, fine-tuning process, and natural\\nlanguage generation. The wide arbitrariness at different levels raises concerns\\nabout using LLM in research practice.\",\"PeriodicalId\":501285,\"journal\":{\"name\":\"arXiv - CS - Digital Libraries\",\"volume\":\"18 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-05-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv - CS - Digital Libraries\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/arxiv-2405.03007\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - CS - Digital Libraries","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2405.03007","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

大型文献计量数据库,如 Web of Science、Scopus 和 OpenAlex,为文献计量分析提供了便利,但也具有执行性,影响了科学产出的可见性和参与实体的影响衡量。最近,这些数据库在各自的分类中采用了联合国的可持续发展目标(SDGs),这些目标因其不同的性质而受到批评。这项工作建议利用大型语言模型(LLM)的特点,通过探索五项可持续发展目标,了解不同的可持续发展目标分类给文献计量数据带来的 "数据偏差"。我们建立了一个大型语言模型(LLM),该模型可根据数据库的 SDG 分类中的不同 SDG 分类进行并行微调。我们的研究结果表明,在模型架构、分类出版物、微调过程和自然语言生成方面都具有很高的灵敏度。不同层面的广泛任意性引起了人们对在研究实践中使用 LLM 的担忧。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
On the performativity of SDG classifications in large bibliometric databases
Large bibliometric databases, such as Web of Science, Scopus, and OpenAlex, facilitate bibliometric analyses, but are performative, affecting the visibility of scientific outputs and the impact measurement of participating entities. Recently, these databases have taken up the UN's Sustainable Development Goals (SDGs) in their respective classifications, which have been criticised for their diverging nature. This work proposes using the feature of large language models (LLMs) to learn about the "data bias" injected by diverse SDG classifications into bibliometric data by exploring five SDGs. We build a LLM that is fine-tuned in parallel by the diverse SDG classifications inscribed into the databases' SDG classifications. Our results show high sensitivity in model architecture, classified publications, fine-tuning process, and natural language generation. The wide arbitrariness at different levels raises concerns about using LLM in research practice.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信