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Liu, X.; Bu, Y.; Li, M.; Li, J.: Monodisciplinary collaboration disrupts science more than multidisciplinary collaboration (2024)
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- Abstract
- Collaboration across disciplines is a critical form of scientific collaboration to solve complex problems and make innovative contributions. This study focuses on the association between multidisciplinary collaboration measured by coauthorship in publications and the disruption of publications measured by the Disruption (D) index. We used authors' affiliations as a proxy of the disciplines to which they belong and categorized an article into multidisciplinary collaboration or monodisciplinary collaboration. The D index quantifies the extent to which a study disrupts its predecessors. We selected 13 journals that publish articles in six disciplines from the Microsoft Academic Graph (MAG) database and then constructed regression models with fixed effects and estimated the relationship between the variables. The findings show that articles with monodisciplinary collaboration are more disruptive than those with multidisciplinary collaboration. Furthermore, we uncovered the mechanism of how monodisciplinary collaboration disrupts science more than multidisciplinary collaboration by exploring the references of the sampled publications.
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Zhang, X.; Fang, Y.; He, W.; Zhang, Y.; Liu, X.: Epistemic motivation, task reflexivity, and knowledge contribution behavior on team wikis : a cross-level moderation model (2019)
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Liu, X.; Chen, X.: Authors' noninstitutional emails and their correlation with retraction (2021)
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- Abstract
- We collected research articles from Retraction Watch database, Scopus, and a major retraction announcement by Springer, to identify emails used by authors. Authors' emails can be institutional emails and noninstitutional emails. Data suggest that retracted articles are more likely to use noninstitutional emails, but it is difficult to generalize. The study put some focus on authors from China.
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Zhang, C.; Liu, X.; Xu, Y.(C.); Wang, Y.: Quality-structure index : a new metric to measure scientific journal influence (2011)
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Cui, Y.; Wang, Y.; Liu, X.; Wang, X.; Zhang, X.: Multidimensional scholarly citations : characterizing and understanding scholars' citation behaviors (2023)
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Yang, Y.; Liu, X.: ¬A re-examination of text categorization methods (1999)
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Liu, X.; Yu, S.; Janssens, F.; Glänzel, W.; Moreau, Y.; Moor, B.de: Weighted hybrid clustering by combining text mining and bibliometrics on a large-scale journal database (2010)
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Chen, Z.; Huang, Y.; Tian, J.; Liu, X.; Fu, K.; Huang, T.: Joint model for subsentence-level sentiment analysis with Markov logic (2015)
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Jiang, Z.; Liu, X.; Chen, Y.: Recovering uncaptured citations in a scholarly network : a two-step citation analysis to estimate publication importance (2016)
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Liu, X.; Guo, C.; Zhang, L.: Scholar metadata and knowledge generation with human and artificial intelligence (2014)
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- Abstract
- Scholar metadata have traditionally centered on descriptive representations, which have been used as a foundation for scholarly publication repositories and academic information retrieval systems. In this article, we propose innovative and economic methods of generating knowledge-based structural metadata (structural keywords) using a combination of natural language processing-based machine-learning techniques and human intelligence. By allowing low-barrier participation through a social media system, scholars (both as authors and users) can participate in the metadata editing and enhancing process and benefit from more accurate and effective information retrieval. Our experimental web system ScholarWiki uses machine learning techniques, which automatically produce increasingly refined metadata by learning from the structural metadata contributed by scholars. The cumulated structural metadata add intelligence and automatically enhance and update recursively the quality of metadata, wiki pages, and the machine-learning model.
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Chen, M.; Liu, X.; Qin, J.: Semantic relation extraction from socially-generated tags : a methodology for metadata generation (2008)
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- Source
- Metadata for semantic and social applications : proceedings of the International Conference on Dublin Core and Metadata Applications, Berlin, 22 - 26 September 2008, DC 2008: Berlin, Germany / ed. by Jane Greenberg and Wolfgang Klas
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Liu, X.; Qin, J.: ¬An interactive metadata model for structural, descriptive, and referential representation of scholarly output (2014)
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- Abstract
- The scientific metadata model proposed in this article encompasses both classical descriptive metadata such as those defined in the Dublin Core Metadata Element Set (DC) and the innovative structural and referential metadata properties that go beyond the classical model. Structural metadata capture the structural vocabulary in research publications; referential metadata include not only citations but also data about other types of scholarly output that is based on or related to the same publication. The article describes the structural, descriptive, and referential (SDR) elements of the metadata model and explains the underlying assumptions and justifications for each major component in the model. ScholarWiki, an experimental system developed as a proof of concept, was built over the wiki platform to allow user interaction with the metadata and the editing, deleting, and adding of metadata. By allowing and encouraging scholars (both as authors and as users) to participate in the knowledge and metadata editing and enhancing process, the larger community will benefit from more accurate and effective information retrieval. The ScholarWiki system utilizes machine-learning techniques that can automatically produce self-enhanced metadata by learning from the structural metadata that scholars contribute, which will add intelligence to enhance and update automatically the publication of metadata Wiki pages.
