Geographies of influence: a citation network analysis of Higher Education 1972–2014
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- 作者：Angelito Calma ; Martin Davies
- 关键词：Citation analysis ; Higher education journal ; Network analysis ; Gephi™Bibliometric analysis
- 刊名：Scientometrics
- 出版年：2017
- 出版时间：March 2017
- 年：2017
- 卷：110
- 期：3
- 页码：1579-1599
- 全文大小：
- 刊物主题：Information Storage and Retrieval; Library Science;
- 出版者：Springer Netherlands
- ISSN：1588-2861
- 卷排序：110
文摘
This paper provides a citation network analysis of the publications of the journal Higher Education from 1972 to 2014 inclusive. This represents nearly the entire history of the journal. It analyses the most published authors and the most cited articles, as well as the most cited authors. This data includes the highest number of publications both by institution and country of origin. 2176 articles were taken from Web of Science™ as a source of primary data. These articles were found to have 68,009 references. Analysis was carried out using the Web of Science™ online analytics tool and Excel®. Gephi™, a data visualisation and manipulation software, was then used to provide visual representations of the associated citation networks. These representations were shown to constitute “terrains” of citations or “geographies of influence”—effectively bringing to bear empirical data in support of Macfarlane’s higher education research “archipelago”. Nationality biases were observed between US and UK/European/Australian higher education journals. Results indicate that the most published authors throughout the journal’s history are Meyer, Kember, Richardson, Enders and Prosser. Confirming earlier studies on UK and Australian journals, the five most cited authors are Entwistle, Clark, Marton, Biggs and Ramsden. The single most cited article is Clark’s 1983 Higher education system: academic organization in cross-national perspective. The top publication years for the journal were 2012, 2009 and 2011. Results from this paper shed light into the evolving concerns of the journal and its readership, and provide a demonstration of a powerful way of analysing citation data.