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Can we use Google Scholar to identify highly-cited documents?

机译:我们可以使用Google学术搜索来识别高被引文档吗?

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The main objective of this paper is to empirically test whether the identification of highly cited documents through Google Scholar is feasible and reliable. To this end, we carried out a longitudinal analysis (1950-2013), running a generic query (filtered only by year of publication) to minimise the effects of academic search engine optimisation. This gave us a final sample of 64,000 documents (1000 per year). The strong correlation between a document's citations and its position in the search results (r = 0.67) led us to conclude that Google Scholar is able to identify highly-cited papers effectively. This, combined with Google Scholar's unique coverage (no restrictions on document type and source), makes the academic search engine an invaluable tool for bibliometric research relating to the identification of the most influential scientific documents. We find evidence, however, that Google Scholar ranks those documents whose language (or geographical web domain) matches with the user's interface language higher than could be expected based on citations. Nonetheless, this language effect and other factors related to the Google Scholar's operation, i.e. the proper identification of versions and the date of publication, only have an incidental impact. They do not compromise the ability of Google Scholar to identify the highly-cited papers. (C) 2016 Elsevier Ltd. All rights reserved.
机译:本文的主要目的是通过经验测试通过Google Scholar识别高被引文档是否可行和可靠。为此,我们进行了纵向分析(1950年至2013年),运行了一个通用查询(仅按发布年份进行过滤),以最大程度地降低学术搜索引擎优化的影响。这使我们最终获得了64,000个文档的样本(每年1000个)。文档的引文与其在搜索结果中的位置之间密切相关(r = 0.67),这使我们得出结论,即Google学术搜索能够有效地识别高被引论文。结合Google Scholar独特的覆盖范围(对文档类型和来源没有限制),学术搜索引擎成为了与鉴定最具影响力的科学文献有关的文献计量研究的宝贵工具。但是,我们发现有证据表明,Google学术搜索对那些语言(或地理网络域)与用户界面语言相匹配的文档的排名高于基于引文的预期。但是,这种语言影响和与Google学术搜索操作相关的其他因素(即正确标识版本和发布日期)仅会产生附带影响。它们不会影响Google学术搜索识别高被引论文的能力。 (C)2016 Elsevier Ltd.保留所有权利。

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