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A systematic method for identifying references to academic research in grey literature
Grey literature encompasses documents not published in academic journals or books. Some grey literature has substantial societal importance, such as medical guidelines, government analyses and pressure group reports. Academic research cited in such documents may therefore have had indirect societal...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9223253/ https://www.ncbi.nlm.nih.gov/pubmed/35765540 http://dx.doi.org/10.1007/s11192-022-04408-4 |
_version_ | 1784733081445335040 |
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author | Bickley, Matthew S. Kousha, Kayvan Thelwall, Michael |
author_facet | Bickley, Matthew S. Kousha, Kayvan Thelwall, Michael |
author_sort | Bickley, Matthew S. |
collection | PubMed |
description | Grey literature encompasses documents not published in academic journals or books. Some grey literature has substantial societal importance, such as medical guidelines, government analyses and pressure group reports. Academic research cited in such documents may therefore have had indirect societal impact, such as in policy making, clinical practice or legislation. Identifying citations to academic research from grey literature may therefore help assess its societal impacts. This is difficult, however, due to the variety of document and referencing formats used in grey literature, even from a single organisation. In response, this study introduces and tests a semi-automatic method to match academic journal articles with unstandardised grey literature cited references. For this, the metadata (lead author last name, title, year) of 2.45 million UK Russell Group university outputs was matched against a 100-document sample of UK government grey literature to assess the accuracy of 21 matching heuristics. The optimal method (lead author last name and title in either order, maximum of 200 characters apart) is sufficiently accurate and scalable to make the task of matching research outputs to grey literature references feasible. The method was then applied to 3347 government publications, showing approximately 23% of UK government grey literature in this study contained at least one reference to UK Russell Group university output, and of this grey literature, an average of 3.79 references were present per document. The applied method also shows that economics and environmental science academic research is most cited between 2010 and 2018. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11192-022-04408-4. |
format | Online Article Text |
id | pubmed-9223253 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-92232532022-06-24 A systematic method for identifying references to academic research in grey literature Bickley, Matthew S. Kousha, Kayvan Thelwall, Michael Scientometrics Article Grey literature encompasses documents not published in academic journals or books. Some grey literature has substantial societal importance, such as medical guidelines, government analyses and pressure group reports. Academic research cited in such documents may therefore have had indirect societal impact, such as in policy making, clinical practice or legislation. Identifying citations to academic research from grey literature may therefore help assess its societal impacts. This is difficult, however, due to the variety of document and referencing formats used in grey literature, even from a single organisation. In response, this study introduces and tests a semi-automatic method to match academic journal articles with unstandardised grey literature cited references. For this, the metadata (lead author last name, title, year) of 2.45 million UK Russell Group university outputs was matched against a 100-document sample of UK government grey literature to assess the accuracy of 21 matching heuristics. The optimal method (lead author last name and title in either order, maximum of 200 characters apart) is sufficiently accurate and scalable to make the task of matching research outputs to grey literature references feasible. The method was then applied to 3347 government publications, showing approximately 23% of UK government grey literature in this study contained at least one reference to UK Russell Group university output, and of this grey literature, an average of 3.79 references were present per document. The applied method also shows that economics and environmental science academic research is most cited between 2010 and 2018. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11192-022-04408-4. Springer International Publishing 2022-06-23 2022 /pmc/articles/PMC9223253/ /pubmed/35765540 http://dx.doi.org/10.1007/s11192-022-04408-4 Text en © Akadémiai Kiadó, Budapest, Hungary 2022 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Article Bickley, Matthew S. Kousha, Kayvan Thelwall, Michael A systematic method for identifying references to academic research in grey literature |
title | A systematic method for identifying references to academic research in grey literature |
title_full | A systematic method for identifying references to academic research in grey literature |
title_fullStr | A systematic method for identifying references to academic research in grey literature |
title_full_unstemmed | A systematic method for identifying references to academic research in grey literature |
title_short | A systematic method for identifying references to academic research in grey literature |
title_sort | systematic method for identifying references to academic research in grey literature |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9223253/ https://www.ncbi.nlm.nih.gov/pubmed/35765540 http://dx.doi.org/10.1007/s11192-022-04408-4 |
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