| （1.华南师范大学 地理科学学院，广东 广州 510631；2.华南师范大学 经济与管理学院，广东 广州 510006） 摘 要：根据《体育学刊》2001—2017年刊载文献被引用数据信息，借助Ucinet社会网络分析和SPSS统计分析等方法探讨体育学科知识创新溢出基本计量特征和溢出网络图谱特征。研究表明：(1)距发表年份时间越长，体育学科知识创新溢出的强度越弱，溢出高峰期出现在文献发表后第3年前后。(2)学科分类和学科发展水平、第一作者学术水平，影响体育学科知识创新溢出能力；经济发展水平影响对体育学科知识创新溢出的吸收能力。(3)体育学科知识创新溢出网络具有明显的“小世界”特征。(4)《体育学刊》在保持传统载文特色的同时，应密切追踪反映学科发展前沿的研究热点，重视载文学科价值和作者学术影响，形成辐射面广、联系紧密的知识溢出网络。 |
LI Wen-hui1，LI Xue-ying2，QIU Yu-jie2，LI Qing-xia1
| （1.School of Geography，South China Normal University，Guangzhou 510631，China；2.School of Economics and Management，South China Normal University，Guangzhou 510006，China) Abstract: According to the data of citation of the papers published in the Journal of Physical Education between 2001 and 2017, with the help of Ucinet social network analysis method and SPSS statistical analysis method, the authors analyzed the basic measurement characteristics and spillover network graph characteristics of physical education disci-plinary knowledge innovation spillovers, and revealed the following findings: 1) the longer the time from the year of publication, the weaker the intensity of physical education disciplinary knowledge innovation spillovers, the spillover peak period occurred around the third year after literature publication; 2) discipline classification and the level of disci-pline development, and the first author’s academic performance, affected the ability of physical education disciplinary knowledge innovation spillovers; the level of economic development affected the absorption ability of physical educa-tion disciplinary knowledge innovation spillovers; 3) the network of physical education disciplinary knowledge innova-tion spillovers had obvious “small world” characteristics; 4) while maintaining the traditional features of the published papers, the Journal of Physical Education should closely track research hot topics that reflect the frontiers of discipli-nary development, value the disciplinary value of the published papers and the academic influence of the authors, and form a widely radiating and closely connected knowledge spillover network. |
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