Comparing paper level classifications across different methods and systems: an investigation of Nature publications
Abstract The classification of scientific literature into appropriate disciplines is an essential precondition of valid scientometric analysis and significant to the practice of research assessment. In this paper, we compared the classification of publications in Nature based on three different appr...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Zhang, Lin [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
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| Anmerkung: |
© Akadémiai Kiadó, Budapest, Hungary 2022 |
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| Übergeordnetes Werk: |
Enthalten in: Scientometrics - Springer International Publishing, 1978, 127(2022), 12 vom: 26. März, Seite 7633-7651 |
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| Übergeordnetes Werk: |
volume:127 ; year:2022 ; number:12 ; day:26 ; month:03 ; pages:7633-7651 |
| Links: |
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| DOI / URN: |
10.1007/s11192-022-04352-3 |
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OLC2080109553 |
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| 520 | |a Abstract The classification of scientific literature into appropriate disciplines is an essential precondition of valid scientometric analysis and significant to the practice of research assessment. In this paper, we compared the classification of publications in Nature based on three different approaches across three different systems. These were: Web of Science (WoS) subject categories (SCs) provided by InCites, which are based on the disciplinary affiliation of the majority of a paper’s references; Fields of Research (FoR) classification provided by Dimensions, which are derived from machine learning techniques; and subjects classification provided by Springer Nature, which are based on author-selected subject terms in the publisher’s tagging system. The results show, first, that the single category assignment in InCites is not appropriate for a large number of papers. Second, only 27% of papers share the same fields between FoR classification in Dimensions and subjects classification in Springer Nature, revealing great inconsistencies between these machine-determined versus human-judged approaches. Being aware of the characteristics and limitations of the ways we categorize research publications is important to research management. | ||
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10.1007/s11192-022-04352-3 doi (DE-627)OLC2080109553 (DE-He213)s11192-022-04352-3-p DE-627 ger DE-627 rakwb eng 050 370 VZ 11 ssgn Zhang, Lin verfasserin (orcid)0000-0003-0526-9677 aut Comparing paper level classifications across different methods and systems: an investigation of Nature publications 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Akadémiai Kiadó, Budapest, Hungary 2022 Abstract The classification of scientific literature into appropriate disciplines is an essential precondition of valid scientometric analysis and significant to the practice of research assessment. In this paper, we compared the classification of publications in Nature based on three different approaches across three different systems. These were: Web of Science (WoS) subject categories (SCs) provided by InCites, which are based on the disciplinary affiliation of the majority of a paper’s references; Fields of Research (FoR) classification provided by Dimensions, which are derived from machine learning techniques; and subjects classification provided by Springer Nature, which are based on author-selected subject terms in the publisher’s tagging system. The results show, first, that the single category assignment in InCites is not appropriate for a large number of papers. Second, only 27% of papers share the same fields between FoR classification in Dimensions and subjects classification in Springer Nature, revealing great inconsistencies between these machine-determined versus human-judged approaches. Being aware of the characteristics and limitations of the ways we categorize research publications is important to research management. Paper level classifications WoS subject categories Fields of research (FoR) Nature subjects; InCites; Dimensions; Springer Nature Sun, Beibei aut Shu, Fei aut Huang, Ying aut Enthalten in Scientometrics Springer International Publishing, 1978 127(2022), 12 vom: 26. März, Seite 7633-7651 (DE-627)13005352X (DE-600)435652-4 (DE-576)015591697 0138-9130 nnns volume:127 year:2022 number:12 day:26 month:03 pages:7633-7651 https://doi.org/10.1007/s11192-022-04352-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-BUB SSG-OLC-HSW SSG-OPC-BBI GBV_ILN_4012 AR 127 2022 12 26 03 7633-7651 |
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10.1007/s11192-022-04352-3 doi (DE-627)OLC2080109553 (DE-He213)s11192-022-04352-3-p DE-627 ger DE-627 rakwb eng 050 370 VZ 11 ssgn Zhang, Lin verfasserin (orcid)0000-0003-0526-9677 aut Comparing paper level classifications across different methods and systems: an investigation of Nature publications 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Akadémiai Kiadó, Budapest, Hungary 2022 Abstract The classification of scientific literature into appropriate disciplines is an essential precondition of valid scientometric analysis and significant to the practice of research assessment. In this paper, we compared the classification of publications in Nature based on three different approaches across three different systems. These were: Web of Science (WoS) subject categories (SCs) provided by InCites, which are based on the disciplinary affiliation of the majority of a paper’s references; Fields of Research (FoR) classification provided by Dimensions, which are derived from machine learning techniques; and subjects classification provided by Springer Nature, which are based on author-selected subject terms in the publisher’s tagging system. The results show, first, that the single category assignment in InCites is not appropriate for a large number of papers. Second, only 27% of papers share the same fields between FoR classification in Dimensions and subjects classification in Springer Nature, revealing great inconsistencies between these machine-determined versus human-judged approaches. Being aware of the characteristics and limitations of the ways we categorize research publications is important to research management. Paper level classifications WoS subject categories Fields of research (FoR) Nature subjects; InCites; Dimensions; Springer Nature Sun, Beibei aut Shu, Fei aut Huang, Ying aut Enthalten in Scientometrics Springer International Publishing, 1978 127(2022), 12 vom: 26. März, Seite 7633-7651 (DE-627)13005352X (DE-600)435652-4 (DE-576)015591697 0138-9130 nnns volume:127 year:2022 number:12 day:26 month:03 pages:7633-7651 https://doi.org/10.1007/s11192-022-04352-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-BUB SSG-OLC-HSW SSG-OPC-BBI GBV_ILN_4012 AR 127 2022 12 26 03 7633-7651 |
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10.1007/s11192-022-04352-3 doi (DE-627)OLC2080109553 (DE-He213)s11192-022-04352-3-p DE-627 ger DE-627 rakwb eng 050 370 VZ 11 ssgn Zhang, Lin verfasserin (orcid)0000-0003-0526-9677 aut Comparing paper level classifications across different methods and systems: an investigation of Nature publications 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Akadémiai Kiadó, Budapest, Hungary 2022 Abstract The classification of scientific literature into appropriate disciplines is an essential precondition of valid scientometric analysis and significant to the practice of research assessment. In this paper, we compared the classification of publications in Nature based on three different approaches across three different systems. These were: Web of Science (WoS) subject categories (SCs) provided by InCites, which are based on the disciplinary affiliation of the majority of a paper’s references; Fields of Research (FoR) classification provided by Dimensions, which are derived from machine learning techniques; and subjects classification provided by Springer Nature, which are based on author-selected subject terms in the publisher’s tagging system. The results show, first, that the single category assignment in InCites is not appropriate for a large number of papers. Second, only 27% of papers share the same fields between FoR classification in Dimensions and subjects classification in Springer Nature, revealing great inconsistencies between these machine-determined versus human-judged approaches. Being aware of the characteristics and limitations of the ways we categorize research publications is important to research management. Paper level classifications WoS subject categories Fields of research (FoR) Nature subjects; InCites; Dimensions; Springer Nature Sun, Beibei aut Shu, Fei aut Huang, Ying aut Enthalten in Scientometrics Springer International Publishing, 1978 127(2022), 12 vom: 26. März, Seite 7633-7651 (DE-627)13005352X (DE-600)435652-4 (DE-576)015591697 0138-9130 nnns volume:127 year:2022 number:12 day:26 month:03 pages:7633-7651 https://doi.org/10.1007/s11192-022-04352-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-BUB SSG-OLC-HSW SSG-OPC-BBI GBV_ILN_4012 AR 127 2022 12 26 03 7633-7651 |
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10.1007/s11192-022-04352-3 doi (DE-627)OLC2080109553 (DE-He213)s11192-022-04352-3-p DE-627 ger DE-627 rakwb eng 050 370 VZ 11 ssgn Zhang, Lin verfasserin (orcid)0000-0003-0526-9677 aut Comparing paper level classifications across different methods and systems: an investigation of Nature publications 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Akadémiai Kiadó, Budapest, Hungary 2022 Abstract The classification of scientific literature into appropriate disciplines is an essential precondition of valid scientometric analysis and significant to the practice of research assessment. In this paper, we compared the classification of publications in Nature based on three different approaches across three different systems. These were: Web of Science (WoS) subject categories (SCs) provided by InCites, which are based on the disciplinary affiliation of the majority of a paper’s references; Fields of Research (FoR) classification provided by Dimensions, which are derived from machine learning techniques; and subjects classification provided by Springer Nature, which are based on author-selected subject terms in the publisher’s tagging system. The results show, first, that the single category assignment in InCites is not appropriate for a large number of papers. Second, only 27% of papers share the same fields between FoR classification in Dimensions and subjects classification in Springer Nature, revealing great inconsistencies between these machine-determined versus human-judged approaches. Being aware of the characteristics and limitations of the ways we categorize research publications is important to research management. Paper level classifications WoS subject categories Fields of research (FoR) Nature subjects; InCites; Dimensions; Springer Nature Sun, Beibei aut Shu, Fei aut Huang, Ying aut Enthalten in Scientometrics Springer International Publishing, 1978 127(2022), 12 vom: 26. März, Seite 7633-7651 (DE-627)13005352X (DE-600)435652-4 (DE-576)015591697 0138-9130 nnns volume:127 year:2022 number:12 day:26 month:03 pages:7633-7651 https://doi.org/10.1007/s11192-022-04352-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-BUB SSG-OLC-HSW SSG-OPC-BBI GBV_ILN_4012 AR 127 2022 12 26 03 7633-7651 |
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10.1007/s11192-022-04352-3 doi (DE-627)OLC2080109553 (DE-He213)s11192-022-04352-3-p DE-627 ger DE-627 rakwb eng 050 370 VZ 11 ssgn Zhang, Lin verfasserin (orcid)0000-0003-0526-9677 aut Comparing paper level classifications across different methods and systems: an investigation of Nature publications 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Akadémiai Kiadó, Budapest, Hungary 2022 Abstract The classification of scientific literature into appropriate disciplines is an essential precondition of valid scientometric analysis and significant to the practice of research assessment. In this paper, we compared the classification of publications in Nature based on three different approaches across three different systems. These were: Web of Science (WoS) subject categories (SCs) provided by InCites, which are based on the disciplinary affiliation of the majority of a paper’s references; Fields of Research (FoR) classification provided by Dimensions, which are derived from machine learning techniques; and subjects classification provided by Springer Nature, which are based on author-selected subject terms in the publisher’s tagging system. The results show, first, that the single category assignment in InCites is not appropriate for a large number of papers. Second, only 27% of papers share the same fields between FoR classification in Dimensions and subjects classification in Springer Nature, revealing great inconsistencies between these machine-determined versus human-judged approaches. Being aware of the characteristics and limitations of the ways we categorize research publications is important to research management. Paper level classifications WoS subject categories Fields of research (FoR) Nature subjects; InCites; Dimensions; Springer Nature Sun, Beibei aut Shu, Fei aut Huang, Ying aut Enthalten in Scientometrics Springer International Publishing, 1978 127(2022), 12 vom: 26. März, Seite 7633-7651 (DE-627)13005352X (DE-600)435652-4 (DE-576)015591697 0138-9130 nnns volume:127 year:2022 number:12 day:26 month:03 pages:7633-7651 https://doi.org/10.1007/s11192-022-04352-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-BUB SSG-OLC-HSW SSG-OPC-BBI GBV_ILN_4012 AR 127 2022 12 26 03 7633-7651 |
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Comparing paper level classifications across different methods and systems: an investigation of Nature publications |
| abstract |
Abstract The classification of scientific literature into appropriate disciplines is an essential precondition of valid scientometric analysis and significant to the practice of research assessment. In this paper, we compared the classification of publications in Nature based on three different approaches across three different systems. These were: Web of Science (WoS) subject categories (SCs) provided by InCites, which are based on the disciplinary affiliation of the majority of a paper’s references; Fields of Research (FoR) classification provided by Dimensions, which are derived from machine learning techniques; and subjects classification provided by Springer Nature, which are based on author-selected subject terms in the publisher’s tagging system. The results show, first, that the single category assignment in InCites is not appropriate for a large number of papers. Second, only 27% of papers share the same fields between FoR classification in Dimensions and subjects classification in Springer Nature, revealing great inconsistencies between these machine-determined versus human-judged approaches. Being aware of the characteristics and limitations of the ways we categorize research publications is important to research management. © Akadémiai Kiadó, Budapest, Hungary 2022 |
| abstractGer |
Abstract The classification of scientific literature into appropriate disciplines is an essential precondition of valid scientometric analysis and significant to the practice of research assessment. In this paper, we compared the classification of publications in Nature based on three different approaches across three different systems. These were: Web of Science (WoS) subject categories (SCs) provided by InCites, which are based on the disciplinary affiliation of the majority of a paper’s references; Fields of Research (FoR) classification provided by Dimensions, which are derived from machine learning techniques; and subjects classification provided by Springer Nature, which are based on author-selected subject terms in the publisher’s tagging system. The results show, first, that the single category assignment in InCites is not appropriate for a large number of papers. Second, only 27% of papers share the same fields between FoR classification in Dimensions and subjects classification in Springer Nature, revealing great inconsistencies between these machine-determined versus human-judged approaches. Being aware of the characteristics and limitations of the ways we categorize research publications is important to research management. © Akadémiai Kiadó, Budapest, Hungary 2022 |
| abstract_unstemmed |
Abstract The classification of scientific literature into appropriate disciplines is an essential precondition of valid scientometric analysis and significant to the practice of research assessment. In this paper, we compared the classification of publications in Nature based on three different approaches across three different systems. These were: Web of Science (WoS) subject categories (SCs) provided by InCites, which are based on the disciplinary affiliation of the majority of a paper’s references; Fields of Research (FoR) classification provided by Dimensions, which are derived from machine learning techniques; and subjects classification provided by Springer Nature, which are based on author-selected subject terms in the publisher’s tagging system. The results show, first, that the single category assignment in InCites is not appropriate for a large number of papers. Second, only 27% of papers share the same fields between FoR classification in Dimensions and subjects classification in Springer Nature, revealing great inconsistencies between these machine-determined versus human-judged approaches. Being aware of the characteristics and limitations of the ways we categorize research publications is important to research management. © Akadémiai Kiadó, Budapest, Hungary 2022 |
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| title_short |
Comparing paper level classifications across different methods and systems: an investigation of Nature publications |
| url |
https://doi.org/10.1007/s11192-022-04352-3 |
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false |
| author2 |
Sun, Beibei Shu, Fei Huang, Ying |
| author2Str |
Sun, Beibei Shu, Fei Huang, Ying |
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13005352X |
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| doi_str |
10.1007/s11192-022-04352-3 |
| up_date |
2024-07-04T02:57:14.359Z |
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