A systematic method for identifying references to academic research in grey literature

Abstract 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...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Bickley, Matthew S. [verfasserIn] Kousha, Kayvan Thelwall, Michael

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Grey literature Impact assessment Citation analysis UK government Academic references Bland–Altman analysis

Anmerkung:	© Akadémiai Kiadó, Budapest, Hungary 2022

Übergeordnetes Werk:	Enthalten in: Scientometrics - Dordrecht [u.a.] : Springer Science + Business Media B.V., 1978, 127(2022), 12 vom: 23. Juni, Seite 6913-6933
Übergeordnetes Werk:	volume:127 ; year:2022 ; number:12 ; day:23 ; month:06 ; pages:6913-6933

Links:	Volltext

DOI / URN:	10.1007/s11192-022-04408-4

Katalog-ID:	SPR048807583

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245	1	2	\|a A systematic method for identifying references to academic research in grey literature
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520			\|a Abstract 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.
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700	1		\|a Thelwall, Michael \|0 (orcid)0000-0001-6065-205X \|4 aut
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912			\|a GBV_ILN_32
912			\|a GBV_ILN_39
912			\|a GBV_ILN_40
912			\|a GBV_ILN_60
912			\|a GBV_ILN_62
912			\|a GBV_ILN_63
912			\|a GBV_ILN_69
912			\|a GBV_ILN_70
912			\|a GBV_ILN_73
912			\|a GBV_ILN_74
912			\|a GBV_ILN_90
912			\|a GBV_ILN_95
912			\|a GBV_ILN_100
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_120
912			\|a GBV_ILN_138
912			\|a GBV_ILN_150
912			\|a GBV_ILN_151
912			\|a GBV_ILN_152
912			\|a GBV_ILN_161
912			\|a GBV_ILN_170
912			\|a GBV_ILN_171
912			\|a GBV_ILN_187
912			\|a GBV_ILN_213
912			\|a GBV_ILN_224
912			\|a GBV_ILN_230
912			\|a GBV_ILN_250
912			\|a GBV_ILN_281
912			\|a GBV_ILN_285
912			\|a GBV_ILN_293
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
912			\|a GBV_ILN_636
912			\|a GBV_ILN_702
912			\|a GBV_ILN_2001
912			\|a GBV_ILN_2003
912			\|a GBV_ILN_2004
912			\|a GBV_ILN_2005
912			\|a GBV_ILN_2006
912			\|a GBV_ILN_2007
912			\|a GBV_ILN_2009
912			\|a GBV_ILN_2010
912			\|a GBV_ILN_2011
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_2015
912			\|a GBV_ILN_2020
912			\|a GBV_ILN_2021
912			\|a GBV_ILN_2025
912			\|a GBV_ILN_2026
912			\|a GBV_ILN_2027
912			\|a GBV_ILN_2031
912			\|a GBV_ILN_2034
912			\|a GBV_ILN_2037
912			\|a GBV_ILN_2038
912			\|a GBV_ILN_2039
912			\|a GBV_ILN_2044
912			\|a GBV_ILN_2048
912			\|a GBV_ILN_2049
912			\|a GBV_ILN_2050
912			\|a GBV_ILN_2055
912			\|a GBV_ILN_2056
912			\|a GBV_ILN_2057
912			\|a GBV_ILN_2059
912			\|a GBV_ILN_2061
912			\|a GBV_ILN_2064
912			\|a GBV_ILN_2065
912			\|a GBV_ILN_2068
912			\|a GBV_ILN_2088
912			\|a GBV_ILN_2093
912			\|a GBV_ILN_2106
912			\|a GBV_ILN_2107
912			\|a GBV_ILN_2108
912			\|a GBV_ILN_2110
912			\|a GBV_ILN_2111
912			\|a GBV_ILN_2112
912			\|a GBV_ILN_2113
912			\|a GBV_ILN_2118
912			\|a GBV_ILN_2122
912			\|a GBV_ILN_2129
912			\|a GBV_ILN_2143
912			\|a GBV_ILN_2144
912			\|a GBV_ILN_2147
912			\|a GBV_ILN_2148
912			\|a GBV_ILN_2152
912			\|a GBV_ILN_2153
912			\|a GBV_ILN_2188
912			\|a GBV_ILN_2190
912			\|a GBV_ILN_2232
912			\|a GBV_ILN_2336
912			\|a GBV_ILN_2446
912			\|a GBV_ILN_2470
912			\|a GBV_ILN_2472
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912			\|a GBV_ILN_4125
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912			\|a GBV_ILN_4246
912			\|a GBV_ILN_4249
912			\|a GBV_ILN_4251
912			\|a GBV_ILN_4305
912			\|a GBV_ILN_4306
912			\|a GBV_ILN_4307
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912			\|a GBV_ILN_4323
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912			\|a GBV_ILN_4326
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912			\|a GBV_ILN_4333
912			\|a GBV_ILN_4334
912			\|a GBV_ILN_4335
912			\|a GBV_ILN_4336
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allfields	10.1007/s11192-022-04408-4 doi (DE-627)SPR048807583 (SPR)s11192-022-04408-4-e DE-627 ger DE-627 rakwb eng Bickley, Matthew S. verfasserin (orcid)0000-0002-4585-7919 aut A systematic method for identifying references to academic research in grey literature 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Akadémiai Kiadó, Budapest, Hungary 2022 Abstract 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. Grey literature (dpeaa)DE-He213 Impact assessment (dpeaa)DE-He213 Citation analysis (dpeaa)DE-He213 UK government (dpeaa)DE-He213 Academic references (dpeaa)DE-He213 Bland–Altman analysis (dpeaa)DE-He213 Kousha, Kayvan (orcid)0000-0003-4827-971X aut Thelwall, Michael (orcid)0000-0001-6065-205X aut Enthalten in Scientometrics Dordrecht [u.a.] : Springer Science + Business Media B.V., 1978 127(2022), 12 vom: 23. Juni, Seite 6913-6933 (DE-627)320589099 (DE-600)2018679-4 1588-2861 nnns volume:127 year:2022 number:12 day:23 month:06 pages:6913-6933 https://dx.doi.org/10.1007/s11192-022-04408-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 127 2022 12 23 06 6913-6933
spelling	10.1007/s11192-022-04408-4 doi (DE-627)SPR048807583 (SPR)s11192-022-04408-4-e DE-627 ger DE-627 rakwb eng Bickley, Matthew S. verfasserin (orcid)0000-0002-4585-7919 aut A systematic method for identifying references to academic research in grey literature 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Akadémiai Kiadó, Budapest, Hungary 2022 Abstract 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. Grey literature (dpeaa)DE-He213 Impact assessment (dpeaa)DE-He213 Citation analysis (dpeaa)DE-He213 UK government (dpeaa)DE-He213 Academic references (dpeaa)DE-He213 Bland–Altman analysis (dpeaa)DE-He213 Kousha, Kayvan (orcid)0000-0003-4827-971X aut Thelwall, Michael (orcid)0000-0001-6065-205X aut Enthalten in Scientometrics Dordrecht [u.a.] : Springer Science + Business Media B.V., 1978 127(2022), 12 vom: 23. Juni, Seite 6913-6933 (DE-627)320589099 (DE-600)2018679-4 1588-2861 nnns volume:127 year:2022 number:12 day:23 month:06 pages:6913-6933 https://dx.doi.org/10.1007/s11192-022-04408-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 127 2022 12 23 06 6913-6933
allfields_unstemmed	10.1007/s11192-022-04408-4 doi (DE-627)SPR048807583 (SPR)s11192-022-04408-4-e DE-627 ger DE-627 rakwb eng Bickley, Matthew S. verfasserin (orcid)0000-0002-4585-7919 aut A systematic method for identifying references to academic research in grey literature 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Akadémiai Kiadó, Budapest, Hungary 2022 Abstract 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. Grey literature (dpeaa)DE-He213 Impact assessment (dpeaa)DE-He213 Citation analysis (dpeaa)DE-He213 UK government (dpeaa)DE-He213 Academic references (dpeaa)DE-He213 Bland–Altman analysis (dpeaa)DE-He213 Kousha, Kayvan (orcid)0000-0003-4827-971X aut Thelwall, Michael (orcid)0000-0001-6065-205X aut Enthalten in Scientometrics Dordrecht [u.a.] : Springer Science + Business Media B.V., 1978 127(2022), 12 vom: 23. Juni, Seite 6913-6933 (DE-627)320589099 (DE-600)2018679-4 1588-2861 nnns volume:127 year:2022 number:12 day:23 month:06 pages:6913-6933 https://dx.doi.org/10.1007/s11192-022-04408-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 127 2022 12 23 06 6913-6933
allfieldsGer	10.1007/s11192-022-04408-4 doi (DE-627)SPR048807583 (SPR)s11192-022-04408-4-e DE-627 ger DE-627 rakwb eng Bickley, Matthew S. verfasserin (orcid)0000-0002-4585-7919 aut A systematic method for identifying references to academic research in grey literature 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Akadémiai Kiadó, Budapest, Hungary 2022 Abstract 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. Grey literature (dpeaa)DE-He213 Impact assessment (dpeaa)DE-He213 Citation analysis (dpeaa)DE-He213 UK government (dpeaa)DE-He213 Academic references (dpeaa)DE-He213 Bland–Altman analysis (dpeaa)DE-He213 Kousha, Kayvan (orcid)0000-0003-4827-971X aut Thelwall, Michael (orcid)0000-0001-6065-205X aut Enthalten in Scientometrics Dordrecht [u.a.] : Springer Science + Business Media B.V., 1978 127(2022), 12 vom: 23. Juni, Seite 6913-6933 (DE-627)320589099 (DE-600)2018679-4 1588-2861 nnns volume:127 year:2022 number:12 day:23 month:06 pages:6913-6933 https://dx.doi.org/10.1007/s11192-022-04408-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 127 2022 12 23 06 6913-6933
allfieldsSound	10.1007/s11192-022-04408-4 doi (DE-627)SPR048807583 (SPR)s11192-022-04408-4-e DE-627 ger DE-627 rakwb eng Bickley, Matthew S. verfasserin (orcid)0000-0002-4585-7919 aut A systematic method for identifying references to academic research in grey literature 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Akadémiai Kiadó, Budapest, Hungary 2022 Abstract 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. Grey literature (dpeaa)DE-He213 Impact assessment (dpeaa)DE-He213 Citation analysis (dpeaa)DE-He213 UK government (dpeaa)DE-He213 Academic references (dpeaa)DE-He213 Bland–Altman analysis (dpeaa)DE-He213 Kousha, Kayvan (orcid)0000-0003-4827-971X aut Thelwall, Michael (orcid)0000-0001-6065-205X aut Enthalten in Scientometrics Dordrecht [u.a.] : Springer Science + Business Media B.V., 1978 127(2022), 12 vom: 23. Juni, Seite 6913-6933 (DE-627)320589099 (DE-600)2018679-4 1588-2861 nnns volume:127 year:2022 number:12 day:23 month:06 pages:6913-6933 https://dx.doi.org/10.1007/s11192-022-04408-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 127 2022 12 23 06 6913-6933
language	English
source	Enthalten in Scientometrics 127(2022), 12 vom: 23. Juni, Seite 6913-6933 volume:127 year:2022 number:12 day:23 month:06 pages:6913-6933
sourceStr	Enthalten in Scientometrics 127(2022), 12 vom: 23. Juni, Seite 6913-6933 volume:127 year:2022 number:12 day:23 month:06 pages:6913-6933
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institution	findex.gbv.de
topic_facet	Grey literature Impact assessment Citation analysis UK government Academic references Bland–Altman analysis
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container_title	Scientometrics
authorswithroles_txt_mv	Bickley, Matthew S. @@aut@@ Kousha, Kayvan @@aut@@ Thelwall, Michael @@aut@@
publishDateDaySort_date	2022-06-23T00:00:00Z
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author	Bickley, Matthew S.
spellingShingle	Bickley, Matthew S. misc Grey literature misc Impact assessment misc Citation analysis misc UK government misc Academic references misc Bland–Altman analysis A systematic method for identifying references to academic research in grey literature
authorStr	Bickley, Matthew S.
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topic_title	A systematic method for identifying references to academic research in grey literature Grey literature (dpeaa)DE-He213 Impact assessment (dpeaa)DE-He213 Citation analysis (dpeaa)DE-He213 UK government (dpeaa)DE-He213 Academic references (dpeaa)DE-He213 Bland–Altman analysis (dpeaa)DE-He213
topic	misc Grey literature misc Impact assessment misc Citation analysis misc UK government misc Academic references misc Bland–Altman analysis
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title	A systematic method for identifying references to academic research in grey literature
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title_full	A systematic method for identifying references to academic research in grey literature
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author_browse	Bickley, Matthew S. Kousha, Kayvan Thelwall, Michael
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author-letter	Bickley, Matthew S.
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title_sort	systematic method for identifying references to academic research in grey literature
title_auth	A systematic method for identifying references to academic research in grey literature
abstract	Abstract 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. © Akadémiai Kiadó, Budapest, Hungary 2022
abstractGer	Abstract 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. © Akadémiai Kiadó, Budapest, Hungary 2022
abstract_unstemmed	Abstract 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. © Akadémiai Kiadó, Budapest, Hungary 2022
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title_short	A systematic method for identifying references to academic research in grey literature
url	https://dx.doi.org/10.1007/s11192-022-04408-4
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author2	Kousha, Kayvan Thelwall, Michael
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doi_str	10.1007/s11192-022-04408-4
up_date	2024-07-03T21:36:07.363Z
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fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR048807583</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230509121910.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">221206s2022 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s11192-022-04408-4</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR048807583</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s11192-022-04408-4-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Bickley, Matthew S.</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0002-4585-7919</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="2"><subfield code="a">A systematic method for identifying references to academic research in grey literature</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Akadémiai Kiadó, Budapest, Hungary 2022</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract 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. 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A systematic method for identifying references to academic research in grey literature

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