{"title":"Comparison of Sustainable Development Goals Labeling Systems based on Topic Coverage","authors":"Li Li, Yu Zhao, Zhesi Shen","doi":"arxiv-2408.13455","DOIUrl":null,"url":null,"abstract":"With the growing importance of sustainable development goals (SDGs), various\nlabeling systems have emerged for effective monitoring and evaluation. This\nstudy assesses six labeling systems across 1.85 million documents at both paper\nlevel and topic level. Our findings indicate that the SDGO and SDSN systems are\nmore aggressive, while systems such as Auckland, Aurora, SIRIS, and Elsevier\nexhibit significant topic consistency, with similarity scores exceeding 0.75\nfor most SDGs. However, similarities at the paper level generally fall short,\nparticularly for specific SDGs like SDG 10. We highlight the crucial role of\ncontextual information in keyword-based labeling systems, noting that\noverlooking context can introduce bias in the retrieval of papers (e.g.,\nvariations in \"migration\" between biomedical and geographical contexts). These\nresults reveal substantial discrepancies among SDG labeling systems,\nemphasizing the need for improved methodologies to enhance the accuracy and\nrelevance of SDG evaluations.","PeriodicalId":501285,"journal":{"name":"arXiv - CS - Digital Libraries","volume":"167 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-08-24","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-2408.13455","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
With the growing importance of sustainable development goals (SDGs), various
labeling systems have emerged for effective monitoring and evaluation. This
study assesses six labeling systems across 1.85 million documents at both paper
level and topic level. Our findings indicate that the SDGO and SDSN systems are
more aggressive, while systems such as Auckland, Aurora, SIRIS, and Elsevier
exhibit significant topic consistency, with similarity scores exceeding 0.75
for most SDGs. However, similarities at the paper level generally fall short,
particularly for specific SDGs like SDG 10. We highlight the crucial role of
contextual information in keyword-based labeling systems, noting that
overlooking context can introduce bias in the retrieval of papers (e.g.,
variations in "migration" between biomedical and geographical contexts). These
results reveal substantial discrepancies among SDG labeling systems,
emphasizing the need for improved methodologies to enhance the accuracy and
relevance of SDG evaluations.