Search (210 results, page 1 of 11)

  • × language_ss:"e"
  • × year_i:[2020 TO 2030}
  1. Zhao, Y.(C.); Du, J.T.; Pang, N.; Raju, J.; Yan, H.: JASIST special issue on ICT4D and intersections with the information field : guest editorial (2023) 0.07 
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  2. Shvartzshnaider, Y.; Sanfilippo, M.R.; Apthorpe, N.: GKC-CI : a unifying framework for contextual norms and information governance (2022) 0.05 
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  3. Asubiaro, T.V.; Onaolapo, S.: ¬A comparative study of the coverage of African journals in Web of Science, Scopus, and CrossRef (2023) 0.04 
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    Abstract
    This is the first study that evaluated the coverage of journals from Africa in Web of Science, Scopus, and CrossRef. A list of active journals published in each of the 55 African countries was compiled from Ulrich's periodicals directory and African Journals Online (AJOL) website. Journal master lists for Web of Science, Scopus, and CrossRef were searched for the African journals. A total of 2,229 unique active African journals were identified from Ulrich (N = 2,117, 95.0%) and AJOL (N = 243, 10.9%) after removing duplicates. The volume of African journals in Web of Science and Scopus databases is 7.4% (N = 166) and 7.8% (N = 174), respectively, compared to the 45.6% (N = 1,017) covered in CrossRef. While making up only 17.% of all the African journals, South African journals had the best coverage in the two most authoritative databases, accounting for 73.5% and 62.1% of all the African journals in Web of Science and Scopus, respectively. In contrast, Nigeria published 44.5% of all the African journals. The distribution of the African journals is biased in favor of Medical, Life and Health Sciences and Humanities and the Arts in the three databases. The low representation of African journals in CrossRef, a free indexing infrastructure that could be harnessed for building an African-centric research indexing database, is concerning.
    Date
    22. 6.2023 14:09:06
  4. Wang, S.; Ma, Y.; Mao, J.; Bai, Y.; Liang, Z.; Li, G.: Quantifying scientific breakthroughs by a novel disruption indicator based on knowledge entities : On the rise of scrape-and-report scholarship in online reviews research (2023) 0.04 
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    Date
    22. 1.2023 18:37:33
  5. Jiang, Y.; Meng, R.; Huang, Y.; Lu, W.; Liu, J.: Generating keyphrases for readers : a controllable keyphrase generation framework (2023) 0.04 
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    Date
    22. 6.2023 14:55:20
  6. Ma, Y.: Relatedness and compatibility : the concept of privacy in Mandarin Chinese and American English corpora (2023) 0.03 
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    Date
    22. 1.2023 18:59:40
  7. Milard, B.; Pitarch, Y.: Egocentric cocitation networks and scientific papers destinies (2023) 0.03 
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    Date
    21. 3.2023 19:22:14
  8. Zhang, X.; Wang, D.; Tang, Y.; Xiao, Q.: How question type influences knowledge withholding in social Q&A community (2023) 0.03 
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    Date
    22. 9.2023 13:51:47
  9. Huvila, I.; Enwald, H.; Eriksson-Backa, K.; Liu, Y.-H.; Hirvonen, N.: Information behavior and practices research informing information systems design (2022) 0.03 
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  10. Haimson, O.L.; Carter, A.J.; Corvite, S.; Wheeler, B.; Wang, L.; Liu, T.; Lige, A.: ¬The major life events taxonomy : social readjustment, social media information sharing, and online network separation during times of life transition (2021) 0.03 
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    Abstract
    When people experience major life changes, this often impacts their self-presentation, networks, and online behavior in substantial ways. To effectively study major life transitions and events, we surveyed a large U.S. sample (n = 554) to create the Major Life Events Taxonomy, a list of 121 life events in 12 categories. We then applied this taxonomy to a second large U.S. survey sample (n = 775) to understand on average how much social readjustment each event required, how likely each event was to be shared on social media with different types of audiences, and how much online network separation each involved. We found that social readjustment is positively correlated with sharing on social media, with both broad audiences and close ties as well as in online spaces separate from one's network of known ties. Some life transitions involve high levels of sharing with both separate audiences and broad audiences on social media, providing evidence for what previous research has called social media as social transition machinery. Researchers can use the Major Life Events Taxonomy to examine how people's life transition experiences relate to their behaviors, technology use, and health and well-being outcomes.
    Date
    10. 6.2021 19:22:47
  11. Chi, Y.; He, D.; Jeng, W.: Laypeople's source selection in online health information-seeking process (2020) 0.03 
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    Date
    12.11.2020 13:22:09
  12. Huang, T.; Nie, R.; Zhao, Y.: Archival knowledge in the field of personal archiving : an exploratory study based on grounded theory (2021) 0.03 
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    Date
    22. 1.2021 14:20:27
  13. Oh, H.; Nam, S.; Zhu, Y.: Structured abstract summarization of scientific articles : summarization using full-text section information (2023) 0.03 
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    Date
    22. 1.2023 18:57:12
  14. Belabbes, M.A.; Ruthven, I.; Moshfeghi, Y.; Rasmussen Pennington, D.: Information overload : a concept analysis (2023) 0.03 
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    Date
    22. 4.2023 19:27:56
  15. Zhang, Y.; Liu, J.; Song, S.: ¬The design and evaluation of a nudge-based interface to facilitate consumers' evaluation of online health information credibility (2023) 0.03 
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    22. 6.2023 18:18:34
  16. Zhang, Y.; Wu, M.; Zhang, G.; Lu, J.: Stepping beyond your comfort zone : diffusion-based network analytics for knowledge trajectory recommendation (2023) 0.03 
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    22. 6.2023 18:07:12
  17. Vakkari, P.; Järvelin, K.; Chang, Y.-W.: ¬The association of disciplinary background with the evolution of topics and methods in Library and Information Science research 1995-2015 (2023) 0.03 
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    22. 6.2023 18:15:06
  18. Luo, L.; Ju, J.; Li, Y.-F.; Haffari, G.; Xiong, B.; Pan, S.: ChatRule: mining logical rules with large language models for knowledge graph reasoning (2023) 0.03 
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    Date
    23.11.2023 19:07:22
  19. Agüera y Arcas, B.: Artificial neural networks are making strides towards consciousness (2022) 0.03 
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    Source
    ¬The Economist. 2022, [https://www.economist.com/by-invitation/2022/06/09/artificial-neural-networks-are-making-strides-towards-consciousness-according-to-blaise-aguera-y-arcas?giftId=89e08696-9884-4670-b164-df58fffdf067]
  20. Noever, D.; Ciolino, M.: ¬The Turing deception (2022) 0.03 
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    Source
    https%3A%2F%2Farxiv.org%2Fabs%2F2212.06721&usg=AOvVaw3i_9pZm9y_dQWoHi6uv0EN

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