Search (1235 results, page 1 of 62)

  • × year_i:[2010 TO 2020}
  1. Zeng, Q.; Yu, M.; Yu, W.; Xiong, J.; Shi, Y.; Jiang, M.: Faceted hierarchy : a new graph type to organize scientific concepts and a construction method (2019) 0.08
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    Content
    Vgl.: https%3A%2F%2Faclanthology.org%2FD19-5317.pdf&usg=AOvVaw0ZZFyq5wWTtNTvNkrvjlGA.
  2. Crespo, J.A.; Herranz, N.; Li, Y.; Ruiz-Castillo, J.: ¬The effect on citation inequality of differences in citation practices at the web of science subject category level (2014) 0.07
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    Abstract
    This article studies the impact of differences in citation practices at the subfield, or Web of Science subject category level, using the model introduced in Crespo, Li, and Ruiz-Castillo (2013a), according to which the number of citations received by an article depends on its underlying scientific influence and the field to which it belongs. We use the same Thomson Reuters data set of about 4.4 million articles used in Crespo et al. (2013a) to analyze 22 broad fields. The main results are the following: First, when the classification system goes from 22 fields to 219 subfields the effect on citation inequality of differences in citation practices increases from ?14% at the field level to 18% at the subfield level. Second, we estimate a set of exchange rates (ERs) over a wide [660, 978] citation quantile interval to express the citation counts of articles into the equivalent counts in the all-sciences case. In the fractional case, for example, we find that in 187 of 219 subfields the ERs are reliable in the sense that the coefficient of variation is smaller than or equal to 0.10. Third, in the fractional case the normalization of the raw data using the ERs (or subfield mean citations) as normalization factors reduces the importance of the differences in citation practices from 18% to 3.8% (3.4%) of overall citation inequality. Fourth, the results in the fractional case are essentially replicated when we adopt a multiplicative approach.
  3. Tetens, H.: Zur Erkenntnistheorie des religiösen Gottesglaubens (2018) 0.07
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    Source
    Information Philosophie. 2018. H.1, S.8-16 [https://www.information-philosophie.de/?a=1&t=8679&n=2&y=4&c=131]
  4. Bronstein, J.; Gazit, T.; Perez, O.; Bar-Ilan, J.; Aharony, N.; Amichai-Hamburger, Y.: ¬An examination of the factors contributing to participation in online social platforms (2016) 0.07
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    Date
    20. 1.2015 18:30:22
  5. Huang, Y.; Bu, Y.; Ding, Y.; Lu, W.: From zero to one : a perspective on citing (2019) 0.06
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    Abstract
    This article investigates the lengths of time that publications with different numbers of citations take to receive their first citation (the beginning stage), and then compares the lengths of time to receive two or more citations after receiving the first citation (the accumulative stage) in the field of computer science. We find that in the beginning stage, that is, from zero to one citation, high-, medium-, and low-cited publications do not obviously exhibit different lengths of time. However, in the accumulative stage, that is, from one to N citations, highly cited publications begin to receive citations much more rapidly than medium- and low-cited publications. Moreover, as N increases, the difference in receiving new citations among high-, medium-, and low-cited publications increases quite significantly.
  6. Wolchover, N.: Wie ein Aufsehen erregender Beweis kaum Beachtung fand (2017) 0.06
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    Date
    22. 4.2017 10:42:05
    22. 4.2017 10:48:38
  7. HaCohen-Kerner, Y. et al.: Classification using various machine learning methods and combinations of key-phrases and visual features (2016) 0.06
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    Date
    1. 2.2016 18:25:22
  8. Zhou, Y. et al.: Analysing entity context in multilingual Wikipedia to support entity-centric retrieval applications (2016) 0.06
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    Date
    1. 2.2016 18:25:22
  9. Chen, Y.-N.: ¬An FRBR-based approach for transforming MARC records into linked data (2018) 0.05
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  10. Balog, K.; Schuth, A.; Dekker, P.; Tavakolpoursaleh, N.; Schaer, P.; Chuang, P.-Y.: Overview of the TREC 2016 Open Search track Academic Search Edition (2016) 0.05
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  11. Verwer, K.: Freiheit und Verantwortung bei Hans Jonas (2011) 0.05
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    Content
    Vgl.: http%3A%2F%2Fcreativechoice.org%2Fdoc%2FHansJonas.pdf&usg=AOvVaw1TM3teaYKgABL5H9yoIifA&opi=89978449.
  12. Paul-Hus, A.; Desrochers, N.; Rijcke, S.de; Rushforth, A.D.: ¬The reward system of science (2017) 0.05
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    Date
    20. 1.2015 18:30:22
  13. Rekabsaz, N. et al.: Toward optimized multimodal concept indexing (2016) 0.05
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    Date
    1. 2.2016 18:25:22
  14. Platis, N. et al.: Visualization of uncertainty in tag clouds (2016) 0.05
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    Date
    1. 2.2016 18:25:22
  15. Ford, N.: Introduction to information behaviour (2015) 0.05
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    Date
    22. 1.2017 16:45:48
  16. Häring, N.; Hensinger, P.: "Digitale Bildung" : Der abschüssige Weg zur Konditionierungsanstalt (2019) 0.05
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    Date
    22. 2.2019 11:45:19
  17. Lin, Y.-R.; Margolin, D.; Lazer, D.: Uncovering social semantics from textual traces : a theory-driven approach and evidence from public statements of U.S. Members of Congress (2016) 0.05
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    Abstract
    The increasing abundance of digital textual archives provides an opportunity for understanding human social systems. Yet the literature has not adequately considered the disparate social processes by which texts are produced. Drawing on communication theory, we identify three common processes by which documents might be detectably similar in their textual features-authors sharing subject matter, sharing goals, and sharing sources. We hypothesize that these processes produce distinct, detectable relationships between authors in different kinds of textual overlap. We develop a novel n-gram extraction technique to capture such signatures based on n-grams of different lengths. We test the hypothesis on a corpus where the author attributes are observable: the public statements of the members of the U.S. Congress. This article presents the first empirical finding that shows different social relationships are detectable through the structure of overlapping textual features. Our study has important implications for designing text modeling techniques to make sense of social phenomena from aggregate digital traces.
  18. Onodera, N.; Iwasawa, M.; Midorikawa, N.; Yoshikane, F.; Amano, K.; Ootani, Y.; Kodama, T.; Kiyama, Y.; Tsunoda, H.; Yamazaki, S.: ¬A method for eliminating articles by homonymous authors from the large number of articles retrieved by author search (2011) 0.05
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  19. Kleineberg, M.: Context analysis and context indexing : formal pragmatics in knowledge organization (2014) 0.04
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    Source
    http://www.google.de/url?sa=t&rct=j&q=&esrc=s&source=web&cd=5&ved=0CDQQFjAE&url=http%3A%2F%2Fdigbib.ubka.uni-karlsruhe.de%2Fvolltexte%2Fdocuments%2F3131107&ei=HzFWVYvGMsiNsgGTyoFI&usg=AFQjCNE2FHUeR9oQTQlNC4TPedv4Mo3DaQ&sig2=Rlzpr7a3BLZZkqZCXXN_IA&bvm=bv.93564037,d.bGg&cad=rja
  20. Li, D.; Wang, Y.; Madden, A.; Ding, Y.; Sun, G.G.; Zhang, N.; Zhou, E.: Analyzing stock market trends using social media user moods and social influence (2019) 0.04
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