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新冠疫情之前及期间灰色文献的高学术价值。

The high scholarly value of grey literature before and during Covid-19.

作者信息

Kousha Kayvan, Thelwall Mike, Bickley Matthew

机构信息

Statistical Cybermetrics Research Group, School of Mathematics and Computer Science, University of Wolverhampton, Wulfruna Street, Wolverhampton, WV1 1LY UK.

出版信息

Scientometrics. 2022;127(6):3489-3504. doi: 10.1007/s11192-022-04398-3. Epub 2022 May 21.

DOI:10.1007/s11192-022-04398-3
PMID:35615527
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9122808/
Abstract

UNLABELLED

New academic knowledge in journal articles is partly built on peer reviewed research already published in journals or books. Academics can also draw from non-academic sources, such as the websites of organisations that publish credible information. This article investigates trends in the academic citing of this type of grey literature for 17 health, media, statistics, and large international organisations, with a focus on Covid-19. The results show substantial and steadily increasing numbers of citations to all 17 sites, with larger increases from 2019 to 2020. In 2020, Covid-19 citations to these websites were particularly common for news organisations, the WHO, and the UK Office for National Statistics, apparently for up-to-date information in the rapidly changing circumstances of the pandemic. Except for the UN, the most cited URLs of each organisation were not traditional report-like grey literature but were other types, such as news stories, data, statistics, and general guidance. The Covid-19 citations to most of these websites originated primarily from medical research, commonly for coronavirus data and statistics. Other fields extensively cited some of the non-health websites, as illustrated by social science (including psychology) studies often citing UNESCO. The results confirm that grey literature from major websites has become even more important within academia during the pandemic, providing up-to-date information from credible sources despite a lack of academic peer review. Researchers, reviewers, and editors should accept that it is reasonable to cite this information, when relevant, and evaluators should value academic work that supports these non-academic outputs.

SUPPLEMENTARY INFORMATION

The online version of this article (10.1007/s11192-022-04398-3) contains supplementary material, which is available to authorized users.

摘要

未标注

期刊文章中的新学术知识部分建立在已发表在期刊或书籍中的同行评议研究基础上。学者们也可以借鉴非学术来源,比如发布可靠信息的组织网站。本文调查了17个健康、媒体、统计及大型国际组织这类灰色文献在学术引用方面的趋势,重点关注新冠疫情。结果显示,所有17个网站的被引用次数都大幅且稳步增加,2019年到2020年增长幅度更大。2020年,新闻机构、世界卫生组织和英国国家统计局等网站的新冠疫情相关引用尤为普遍,显然是因为在疫情快速变化的情况下能获取最新信息。除了联合国,每个组织被引用最多的网址并非传统的类似报告的灰色文献,而是其他类型,如新闻报道、数据、统计数据和一般指南。这些网站中大部分关于新冠疫情的引用主要源自医学研究,通常是关于冠状病毒的数据和统计。其他领域也广泛引用了一些非健康类网站,比如社会科学(包括心理学)研究经常引用联合国教科文组织的网站。结果证实,在疫情期间,主要网站的灰色文献在学术界变得更加重要,尽管缺乏学术同行评议,但能提供来自可靠来源的最新信息。研究人员、审稿人和编辑应认可在相关时引用这些信息是合理的,评估人员也应重视支持这些非学术成果的学术工作。

补充信息

本文的在线版本(10.1007/s11192-022-04398-3)包含补充材料,授权用户可获取。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c34d/9122808/2654577dfb96/11192_2022_4398_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c34d/9122808/7c38fb06bf83/11192_2022_4398_Fig1_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c34d/9122808/faa03d6a4f6d/11192_2022_4398_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c34d/9122808/de023325518c/11192_2022_4398_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c34d/9122808/2684a2582d4b/11192_2022_4398_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c34d/9122808/2654577dfb96/11192_2022_4398_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c34d/9122808/7c38fb06bf83/11192_2022_4398_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c34d/9122808/504d172dcc2d/11192_2022_4398_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c34d/9122808/c0196c549aa9/11192_2022_4398_Fig3_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c34d/9122808/de023325518c/11192_2022_4398_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c34d/9122808/2684a2582d4b/11192_2022_4398_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c34d/9122808/2654577dfb96/11192_2022_4398_Fig7_HTML.jpg

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