Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Sistemātiska literatūras apskats ar bibliometrisko palīdzību× | Sistemātiska literatūras apskate× | |
|---|---|---|
| Nozare | Zinātnometrija | Zinātnometrija |
| Saime | Process / pipeline | Process / pipeline |
| Izcelsmes gads≠ | 2017 | 1993 (Cochrane Collaboration); 2004 (Kitchenham SLR guidelines) |
| Autors≠ | Massimo Aria & Corrado Cuccurullo (bibliometrix R package) | Archie Cochrane (conceptual foundation); formalized by the Cochrane Collaboration (1993) and Barbara Kitchenham in software engineering (2004) |
| Tips≠ | Software-assisted systematic review | Evidence synthesis methodology |
| Pirmavots≠ | Aria, M., & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975. DOI ↗ | Kitchenham, B. (2004). Procedures for Performing Systematic Reviews. Keele University Technical Report TR/SE-0401. link ↗ |
| Citi nosaukumi | bibliometrix SLR, R-bibliometrix systematic review, bibliometrix-based literature review, bibliometrix-enhanced SLR | SLR, systematic review, evidence synthesis review, structured literature review |
| Saistītās≠ | 6 | 5 |
| Kopsavilkums≠ | A bibliometrix-assisted systematic literature review integrates the R package bibliometrix — developed by Aria and Cuccurullo (2017) — into the standard systematic review pipeline to automate and visualize bibliometric performance and science-mapping analyses. It combines the transparency and reproducibility of a protocol-driven systematic search with quantitative tools for tracking publication trends, author collaboration networks, keyword co-occurrence, and thematic evolution across a field. | A systematic literature review (SLR) is a structured, reproducible method for identifying, appraising, and synthesizing all relevant studies on a research question. Unlike a narrative review, it follows an explicit, pre-specified protocol — from database search strings through inclusion criteria to data extraction — so that the process is transparent, auditable, and replicable by other researchers. It is widely used in medicine, education, software engineering, and the social sciences to produce the most comprehensive possible evidence base on a topic. |
| ScholarGateDatu kopa ↗ |
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