| 杜化荣,黄镇林,邓宏勇,袁敏,张怀琼,倪力强.研究前沿与热点识别中4种高被引论文遴选模式差异研究[J].数字图书馆论坛,2025,21(1):90~100 |
| 研究前沿与热点识别中4种高被引论文遴选模式差异研究 |
| Differences of Four Selection Models of Highly Cited Papers in Identification of Research Frontiers and Hotspots |
| 投稿时间：2024-10-09 |
| DOI：10.3772/j.issn.1673-2286.2025.01.010 |
| 中文关键词: 高被引论文；前沿热点；差异分析；肝损伤 |
| 英文关键词: Highly Cited Paper; Frontier and Hotspot; Difference Analysis; Liver Injury |
| 基金项目:本研究得到国家重点研发计划“中医药现代化研究”重点专项“基于人工智能大数据的中医药数据分析自动化及可视化呈现研究”
（编号：2019YFC1709803）、上海中医药大学科技发展项目“基于研究前沿贡献与多样性的中医药机构创新竞争力分析”（编号：
23KFW13）资助。 |
| 作者 | 单位 | | 杜化荣 | 上海中医药大学图书馆 | | 黄镇林 | 上海中医药大学中药研究所 | | 邓宏勇 | 上海中医药大学图书馆 | | 袁敏 | 上海中医药大学图书馆 | | 张怀琼 | 上海中医药大学图书馆 | | 倪力强 | 上海中医药大学图书馆 |
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| 中文摘要: |
| 为拓展基于高被引论文的研究前沿热点识别的情报学理论基础和实践应用，对比分析累计被引频次模式（C模式）、出版年-累计被引频次模式（PY-C模式）、年度被引频次模式（YC模式）与出版年-年度被引频次模式（PY-YC模式）4种高被引论文遴选模式的差异，探讨学科领域研究前沿热点识别精度最高的高被引论文遴选模式，解决研究前沿热点识别中数据源头准确性的问题。以肝损伤领域为例，从“新颖性”论文、“上升性”论文、“下降性”论文纳入量的论文角度和特异性热点主题、研究前沿、研究热点数量的主题角度对4种高被引论文遴选模式的差异性进行定量分析。通过理论分析和肝损伤领域的案例研究发现，PY-YC模式可以有效消除“下降性”论文冗余和“新颖性”“上升性”论文缺失的数据噪声对研究前沿热点识别准确度的影响，从而识别出最具前瞻性和潜力的、代表未来发展方向的研究主题。因此，PY-YC模式被认为是识别研究前沿热点时的最优高被引论文遴选模式，可以有效提升研究前沿热点识别中的高被引论文基础数据的准确性。 |
| 英文摘要: |
| In order to expand the theoretical foundations and practical applications of informetric studies for the identification of research frontiers and hotspots based on highly cited papers (HCPs), this paper compares and analyzes the differences of four selection models of HCPs: cumulative citation frequency model (C model), publication year-cumulative citation frequency model (PY-C model), annual citation frequency model (YC model), and publication yearannual citation frequency model (PY-YC model). The aim is to explore the model with the highest accuracy in identifying research frontiers and hotspots in the discipline, addressing the problem of data source accuracy in identifying research frontiers and hotspots. Taking the field of liver injury as an example, this paper explores the differences in the four selection models of HCPs from the paper perspectives of the number of “novelty” papers, “rising” papers, and “declining” papers, as well as the thematic perspectives of the number of exclusively-identified hot topics, research frontiers, and research hotspots. Through theoretical analysis and empirical case studies in the field of liver injury, it is found that the PY-YC model can effectively eliminate the impact of data noise on the accuracy of research frontiers and hotspots identification due to the redundancy of “declining” papers and the lack of “novelty” and “rising” papers, and identify the most forward-looking and potential research topics that represent the future development directions. Thus, the PY-YC model is considered to be the optimal model for selecting HCPs in the identification of research frontiers and hotspots, which effectively improves the accuracy of foundational data of HCPs used in research frontiers and hotspots identification. |
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