Towards indicating interdisciplinarity: Characterizing interdisciplinary knowledge flow
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DOI: 10.1002/asi.24829
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- Shunshun Shi & Lin Zhang & Ying Huang, 2025. "Indicating interdisciplinarity: towards a tentative taxonomy according to problem solving in COVID-19," Scientometrics, Springer;Akadémiai Kiadó, vol. 130(12), pages 6879-6908, December.
- Wu, Renli & Chen, Ruiyang & An, Lu & Chen, Chuanfu, 2025. "Tracing the evolution of library and information science through three anchored dimensions: Library, people, and algorithm," Journal of Informetrics, Elsevier, vol. 19(3).
- Zhang, Yujie & He, Guoxiu & Jiang, Zhuoren, 2025. "Mapping science and revealing disciplinary communication modalities via pre-trained graph neural networks," Journal of Informetrics, Elsevier, vol. 19(4).
- Liu, Kehui & Lu, Quan, 2025. "Identifying high-impact interdisciplinary knowledge flows: An approach combining backward and forward citation analysis," Journal of Informetrics, Elsevier, vol. 19(3).
- Geng, Yu & Yin, Yixian & Cai, Ruonan & Wang, Xianwen, 2026. "Tracing scientific knowledge flow in patents: An explainable machine learning study of citation types and their temporal dynamics," Journal of Informetrics, Elsevier, vol. 20(1).
- Tong Bao & Yi Zhao & Jin Mao & Chengzhi Zhang, 2025. "Examining linguistic shifts in academic writing before and after the launch of ChatGPT: a study on preprint papers," Scientometrics, Springer;Akadémiai Kiadó, vol. 130(7), pages 3597-3627, July.
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