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| Analisis Sitometrik Berbasis Irisan Waktu× | Analisis Ko-Sitasi× | |
|---|---|---|
| Bidang≠ | Saintometrika | Bibliometrika |
| Keluarga | Process / pipeline | Process / pipeline |
| Tahun asal≠ | 1980s–1990s | 1973 |
| Pencetus≠ | Derived from scientometrics tradition; temporal slicing formalized in longitudinal bibliometric studies from the 1980s onward | Henry Small |
| Tipe≠ | Quantitative longitudinal analysis | Method |
| Sumber perintis≠ | Small, H. (1999). Visualizing science by citation mapping. Journal of the American Society for Information Science, 50(9), 799-813. 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 ↗ |
| Alias | temporal scientometrics, period-based scientometric analysis, time-window scientometrics, longitudinal scientometric analysis | co-citation mapping, historiograph, direct citation, citation pair analysis |
| Terkait≠ | 6 | 5 |
| Ringkasan≠ | Time-sliced scientometric analysis divides a bibliographic corpus into discrete temporal windows — commonly five- or ten-year periods — and applies standard scientometric indicators (publication counts, citation rates, h-index, collaboration networks, keyword co-occurrence) within each slice. By comparing results across slices, researchers can reconstruct how a scientific field has grown, shifted focus, formed new collaborations, or declined in influence over time. The approach combines the rigor of quantitative scientometrics with an explicit longitudinal dimension. | 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. |
| ScholarGateSet data ↗ |
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