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  • × author_ss:"Zhang, C."
  • × year_i:[2020 TO 2030}
  1. Yao, X.; Zhang, C.: Global village or virtual balkans? : evolution and performance of scientific collaboration in the information age (2020) 0.00 
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    Abstract
    Scientific collaboration is essential and almost imperative in modern science. However, collaboration may be difficult to achieve because of 2 major barriers: geographic distance and social divides. It is predicted that the advancement of information communication technologies (ICTs) will bring a puzzled conclusion for collaboration in the scientific community: the "Global Village" trend with significantly increased physical distance among collaborated scientists and the "Virtual Balkans" trend with significantly increased social stratification among collaborated scientists. The results of this study reveal that the scientific community evolves towards the Global Village generally on both the geographic and social dimension, but with variations in term of collaboration patterns. The influence of such collaboration patterns on research performance (that is, productivity and impact), however, is asymmetric to each side of collaborators. When researchers from top-tier and general-tier institutions collaborate, researchers from top-tier institutions face a decrease in research productivity and impact, whereas researchers from general-tier institutions increase in research productivity and impact. Furthermore, the development of ICTs plays an important role in shaping the evolving trends and moderating effects of collaboration patterns. Our findings provide a comprehensive understanding of scientific collaboration in the geographic, social, and technological aspect.
    Source
    Journal of the Association for Information Science and Technology. 71(2020) no.4, S.395-408
  2. Xu, H.; Bu, Y.; Liu, M.; Zhang, C.; Sun, M.; Zhang, Y.; Meyer, E.; Salas, E.; Ding, Y.: Team power dynamics and team impact : new perspectives on scientific collaboration using career age as a proxy for team power (2022) 0.00 
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    Abstract
    Power dynamics influence every aspect of scientific collaboration. Team power dynamics can be measured by team power level and team power hierarchy. Team power level is conceptualized as the average level of the possession of resources, expertise, or decision-making authorities of a team. Team power hierarchy represents the vertical differences of the possessions of resources in a team. In Science of Science, few studies have looked at scientific collaboration from the perspective of team power dynamics. This research examines how team power dynamics affect team impact to fill the research gap. In this research, all coauthors of one publication are treated as one team. Team power level and team power hierarchy of one team are measured by the mean and Gini index of career age of coauthors in this team. Team impact is quantified by citations of a paper authored by this team. By analyzing over 7.7 million teams from Science (e.g., Computer Science, Physics), Social Sciences (e.g., Sociology, Library & Information Science), and Arts & Humanities (e.g., Art), we find that flat team structure is associated with higher team impact, especially when teams have high team power level. These findings have been repeated in all five disciplines except Art, and are consistent in various types of teams from Computer Science including teams from industry or academia, teams with different gender groups, teams with geographical contrast, and teams with distinct size.
    Source
    Journal of the Association for Information Science and Technology. 73(2022) no.10, S.1489-1505
  3. Zhang, Y.; Zhang, C.; Li, J.: Joint modeling of characters, words, and conversation contexts for microblog keyphrase extraction (2020) 0.00 
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    Source
    Journal of the Association for Information Science and Technology. 71(2020) no.5, S.553-567
  4. Lu, C.; Zhang, Y.; Ahn, Y.-Y.; Ding, Y.; Zhang, C.; Ma, D.: Co-contributorship network and division of labor in individual scientific collaborations (2020) 0.00 
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    Source
    Journal of the Association for Information Science and Technology. 71(2020) no.10, S.1162-1178
  5. Zhang, Y.; Zhang, C.: Enhancing keyphrase extraction from microblogs using human reading time (2021) 0.00 
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    Source
    Journal of the Association for Information Science and Technology. 72(2021) no.5, S.611-626

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