{"title":"Quantitative Methods in Research Evaluation Citation Indicators, Altmetrics, and Artificial Intelligence","authors":"Mike Thelwall","doi":"arxiv-2407.00135","DOIUrl":null,"url":null,"abstract":"This book critically analyses the value of citation data, altmetrics, and\nartificial intelligence to support the research evaluation of articles,\nscholars, departments, universities, countries, and funders. It introduces and\ndiscusses indicators that can support research evaluation and analyses their\nstrengths and weaknesses as well as the generic strengths and weaknesses of the\nuse of indicators for research assessment. The book includes evidence of the\ncomparative value of citations and altmetrics in all broad academic fields\nprimarily through comparisons against article level human expert judgements\nfrom the UK Research Excellence Framework 2021. It also discusses the potential\napplications of traditional artificial intelligence and large language models\nfor research evaluation, with large scale evidence for the former. The book\nconcludes that citation data can be informative and helpful in some research\nfields for some research evaluation purposes but that indicators are never\naccurate enough to be described as research quality measures. It also argues\nthat AI may be helpful in limited circumstances for some types of research\nevaluation.","PeriodicalId":501285,"journal":{"name":"arXiv - CS - Digital Libraries","volume":"6 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-06-28","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-2407.00135","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
This book critically analyses the value of citation data, altmetrics, and
artificial intelligence to support the research evaluation of articles,
scholars, departments, universities, countries, and funders. It introduces and
discusses indicators that can support research evaluation and analyses their
strengths and weaknesses as well as the generic strengths and weaknesses of the
use of indicators for research assessment. The book includes evidence of the
comparative value of citations and altmetrics in all broad academic fields
primarily through comparisons against article level human expert judgements
from the UK Research Excellence Framework 2021. It also discusses the potential
applications of traditional artificial intelligence and large language models
for research evaluation, with large scale evidence for the former. The book
concludes that citation data can be informative and helpful in some research
fields for some research evaluation purposes but that indicators are never
accurate enough to be described as research quality measures. It also argues
that AI may be helpful in limited circumstances for some types of research
evaluation.