Leandro Rachel Arguello, Michel Angelo Constantino, Antonio Carlos Dorsa, Diego Bezerra de Souza, Flávio Henrique Souza de Araújo, Thiago Teixeira Pereira, Cristiane Martins Viegas de Oliveira
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引用次数: 0
Abstract
This research presents a bibliometric study of scientific productions involving the themes of innovation, networks and emerging jobs in the English language, available in the Web of Science’s database, dating from 1945 to 2020. Seeking to clarify some findings, in the statistical study of Web of Science’s publications, this research demonstrates the orientation of the reasoning of the investigated authors, regarding innovation policies. Of the 49 publications found in a previous screening, we obtained the number of 44 publications in English, which were submitted to statistical, Reinert and Similitude analyses, using the IRAMUTEQ software. During the research, it was possible to notice that this group of words presented a relation in the context of the texts available in the base; that is: the tool presented, statistically, four groups of words with greater relevance, connecting the three themes. In the analysis, IRAMUTEQ presented a division in four clusters (using Reinert’s method) that list the groups of words by their relation of higher incidence and correlation during the study (development, literature, information and form). According to this mapping, it is possible to conclude that the correlation of themes refers to a group of researchers who, in their discourses, connect technological and economic development to relations in an innovation network, and that 50% of the published texts dealt with issues involving the four largest groups of words: development, network, innovation and business.
这项研究对科学成果进行了文献计量学研究,涉及创新、网络和英语新兴工作的主题,可在Web of Science的数据库中找到,时间从1945年到2020年。为了澄清一些发现,在Web of Science出版物的统计研究中,本研究展示了被调查作者关于创新政策的推理取向。在先前筛选的49篇出版物中,我们获得了44篇英文出版物的数量,并使用IRAMUTEQ软件进行统计、Reinert和相似性分析。在研究过程中,可以注意到这组单词在库中可用文本的上下文中呈现出一种关系;也就是说,该工具在统计上呈现了四组相关性更强的单词,将三个主题联系起来。在分析中,IRAMUTEQ提出了四个聚类(使用Reinert的方法),根据研究过程中发生率和相关性较高的词语组(发展、文献、信息和形式)列出。根据这一映射,可以得出结论,主题的相关性是指一组研究人员,在他们的话语中,将技术和经济发展与创新网络中的关系联系起来,并且50%的已发表文本涉及涉及四个最大的词汇组的问题:发展,网络,创新和商业。