方法对比
并排查看您选择的方法;存在差异的行会高亮显示。
| 文献计量学定律:洛特卡定律、布拉德福定律和齐普夫定律× | 共被引分析× | |
|---|---|---|
| 领域 | 文献计量学 | 文献计量学 |
| 方法族 | Process / pipeline | Process / pipeline |
| 起源年份≠ | 1926–1949 | 1973 |
| 提出者≠ | Alfred J. Lotka, Samuel C. Bradford, George K. Zipf | Henry Small |
| 类型≠ | Concept | Method |
| 开创性文献≠ | Lotka, A. J. (1926). The frequency distribution of scientific productivity. Journal of the Washington Academy of Sciences, 16(12), 317–323. link ↗ | Small, H. (1973). Co-citation in the scientific literature: A new measure of the relationship between two documents. Journal of the American Society for Information Science, 24(4), 265–269. DOI ↗ |
| 别名 | bibliometric distributions, productivity laws, frequency laws, information science laws | co-citation mapping, historiograph, direct citation, citation pair analysis |
| 相关≠ | 3 | 5 |
| 摘要≠ | Three foundational empirical laws describe the structure and distribution of scientific information: Lotka's Law characterizes author productivity (most authors publish few papers; a few publish many), Bradford's Law describes journal concentration (a small number of core journals contain the majority of papers on a topic), and Zipf's Law models word and term frequency (word frequency inversely proportional to its rank). These regularities, discovered in the mid-20th century, are remarkably robust across disciplines and have become essential tools for understanding research productivity, organizing information resources, and designing search strategies. | Co-citation analysis is a method that identifies the intellectual structure of a research domain by examining how frequently pairs of documents are cited together in other publications. When two papers are frequently cited together in the literature, they are considered co-cited, indicating they are conceptually related or influential within the same research community. Developed by Henry Small in 1973, co-citation analysis maps the 'invisible colleges' of science—networks of researchers working on related problems—and reveals how knowledge domains evolve over time. |
| ScholarGate数据集 ↗ |
|
|