Comparison of Sustainable Development Goals Labeling Systems based on Topic Coverage
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866909294668021760 |
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| author | Li, Li Zhao, Yu Shen, Zhesi |
| author_facet | Li, Li Zhao, Yu Shen, Zhesi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_13455 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Comparison of Sustainable Development Goals Labeling Systems based on Topic Coverage Li, Li Zhao, Yu Shen, Zhesi Digital Libraries 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. |
| title | Comparison of Sustainable Development Goals Labeling Systems based on Topic Coverage |
| topic | Digital Libraries |
| url | https://arxiv.org/abs/2408.13455 |