Search (255 results, page 1 of 13)

  • × year_i:[2020 TO 2030}
  1. Noever, D.; Ciolino, M.: ¬The Turing deception (2022) 0.17
    0.16673034 = product of:
      0.44461423 = sum of:
        0.06351632 = product of:
          0.19054894 = sum of:
            0.19054894 = weight(_text_:3a in 862) [ClassicSimilarity], result of:
              0.19054894 = score(doc=862,freq=2.0), product of:
                0.33904418 = queryWeight, product of:
                  8.478011 = idf(docFreq=24, maxDocs=44218)
                  0.039991006 = queryNorm
                0.56201804 = fieldWeight in 862, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  8.478011 = idf(docFreq=24, maxDocs=44218)
                  0.046875 = fieldNorm(doc=862)
          0.33333334 = coord(1/3)
        0.19054894 = weight(_text_:2f in 862) [ClassicSimilarity], result of:
          0.19054894 = score(doc=862,freq=2.0), product of:
            0.33904418 = queryWeight, product of:
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.039991006 = queryNorm
            0.56201804 = fieldWeight in 862, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.046875 = fieldNorm(doc=862)
        0.19054894 = weight(_text_:2f in 862) [ClassicSimilarity], result of:
          0.19054894 = score(doc=862,freq=2.0), product of:
            0.33904418 = queryWeight, product of:
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.039991006 = queryNorm
            0.56201804 = fieldWeight in 862, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.046875 = fieldNorm(doc=862)
      0.375 = coord(3/8)
    
    Source
    https%3A%2F%2Farxiv.org%2Fabs%2F2212.06721&usg=AOvVaw3i_9pZm9y_dQWoHi6uv0EN
  2. Gabler, S.: Vergabe von DDC-Sachgruppen mittels eines Schlagwort-Thesaurus (2021) 0.14
    0.13894194 = product of:
      0.37051186 = sum of:
        0.052930266 = product of:
          0.1587908 = sum of:
            0.1587908 = weight(_text_:3a in 1000) [ClassicSimilarity], result of:
              0.1587908 = score(doc=1000,freq=2.0), product of:
                0.33904418 = queryWeight, product of:
                  8.478011 = idf(docFreq=24, maxDocs=44218)
                  0.039991006 = queryNorm
                0.46834838 = fieldWeight in 1000, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  8.478011 = idf(docFreq=24, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=1000)
          0.33333334 = coord(1/3)
        0.1587908 = weight(_text_:2f in 1000) [ClassicSimilarity], result of:
          0.1587908 = score(doc=1000,freq=2.0), product of:
            0.33904418 = queryWeight, product of:
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.039991006 = queryNorm
            0.46834838 = fieldWeight in 1000, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1000)
        0.1587908 = weight(_text_:2f in 1000) [ClassicSimilarity], result of:
          0.1587908 = score(doc=1000,freq=2.0), product of:
            0.33904418 = queryWeight, product of:
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.039991006 = queryNorm
            0.46834838 = fieldWeight in 1000, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              8.478011 = idf(docFreq=24, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1000)
      0.375 = coord(3/8)
    
    Content
    Master thesis Master of Science (Library and Information Studies) (MSc), Universität Wien. Advisor: Christoph Steiner. Vgl.: https://www.researchgate.net/publication/371680244_Vergabe_von_DDC-Sachgruppen_mittels_eines_Schlagwort-Thesaurus. DOI: 10.25365/thesis.70030. Vgl. dazu die Präsentation unter: https://www.google.com/url?sa=i&rct=j&q=&esrc=s&source=web&cd=&ved=0CAIQw7AJahcKEwjwoZzzytz_AhUAAAAAHQAAAAAQAg&url=https%3A%2F%2Fwiki.dnb.de%2Fdownload%2Fattachments%2F252121510%2FDA3%2520Workshop-Gabler.pdf%3Fversion%3D1%26modificationDate%3D1671093170000%26api%3Dv2&psig=AOvVaw0szwENK1or3HevgvIDOfjx&ust=1687719410889597&opi=89978449.
  3. Tang, X.-B.; Fu, W.-G.; Liu, Y.: Knowledge big graph fusing ontology with property graph : a case study of financial ownership network (2021) 0.05
    0.05388907 = product of:
      0.21555628 = sum of:
        0.15359351 = weight(_text_:property in 234) [ClassicSimilarity], result of:
          0.15359351 = score(doc=234,freq=6.0), product of:
            0.25336683 = queryWeight, product of:
              6.335595 = idf(docFreq=212, maxDocs=44218)
              0.039991006 = queryNorm
            0.60621 = fieldWeight in 234, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              6.335595 = idf(docFreq=212, maxDocs=44218)
              0.0390625 = fieldNorm(doc=234)
        0.061962765 = weight(_text_:network in 234) [ClassicSimilarity], result of:
          0.061962765 = score(doc=234,freq=4.0), product of:
            0.17809492 = queryWeight, product of:
              4.4533744 = idf(docFreq=1398, maxDocs=44218)
              0.039991006 = queryNorm
            0.34791988 = fieldWeight in 234, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              4.4533744 = idf(docFreq=1398, maxDocs=44218)
              0.0390625 = fieldNorm(doc=234)
      0.25 = coord(2/8)
    
    Abstract
    The scale of knowledge is growing rapidly in the big data environment, and traditional knowledge organization and services have faced the dilemma of semantic inaccuracy and untimeliness. From a knowledge fusion perspective-combining the precise semantic superiority of traditional ontology with the large-scale graph processing power and the predicate attribute expression ability of property graph-this paper presents an ontology and property graph fusion framework (OPGFF). The fusion process is divided into content layer fusion and constraint layer fusion. The result of the fusion, that is, the knowledge representation model is called knowledge big graph. In addition, this paper applies the knowledge big graph model to the ownership network in the China's financial field and builds a financial ownership knowledge big graph. Furthermore, this paper designs and implements six consistency inference algorithms for finding contradictory data and filling in missing data in the financial ownership knowledge big graph, five of which are completely domain agnostic. The correctness and validity of the algorithms have been experimentally verified with actual data. The fusion OPGFF framework and the implementation method of the knowledge big graph could provide technical reference for big data knowledge organization and services.
  4. Milard, B.; Pitarch, Y.: Egocentric cocitation networks and scientific papers destinies (2023) 0.04
    0.035547473 = product of:
      0.14218989 = sum of:
        0.07435531 = weight(_text_:network in 918) [ClassicSimilarity], result of:
          0.07435531 = score(doc=918,freq=4.0), product of:
            0.17809492 = queryWeight, product of:
              4.4533744 = idf(docFreq=1398, maxDocs=44218)
              0.039991006 = queryNorm
            0.41750383 = fieldWeight in 918, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              4.4533744 = idf(docFreq=1398, maxDocs=44218)
              0.046875 = fieldNorm(doc=918)
        0.067834586 = sum of:
          0.03532521 = weight(_text_:resources in 918) [ClassicSimilarity], result of:
            0.03532521 = score(doc=918,freq=2.0), product of:
              0.14598069 = queryWeight, product of:
                3.650338 = idf(docFreq=3122, maxDocs=44218)
                0.039991006 = queryNorm
              0.2419855 = fieldWeight in 918, product of:
                1.4142135 = tf(freq=2.0), with freq of:
                  2.0 = termFreq=2.0
                3.650338 = idf(docFreq=3122, maxDocs=44218)
                0.046875 = fieldNorm(doc=918)
          0.032509375 = weight(_text_:22 in 918) [ClassicSimilarity], result of:
            0.032509375 = score(doc=918,freq=2.0), product of:
              0.1400417 = queryWeight, product of:
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.039991006 = queryNorm
              0.23214069 = fieldWeight in 918, product of:
                1.4142135 = tf(freq=2.0), with freq of:
                  2.0 = termFreq=2.0
                3.5018296 = idf(docFreq=3622, maxDocs=44218)
                0.046875 = fieldNorm(doc=918)
      0.25 = coord(2/8)
    
    Abstract
    To what extent is the destiny of a scientific paper shaped by the cocitation network in which it is involved? What are the social contexts that can explain these structuring? Using bibliometric data, interviews with researchers, and social network analysis, this article proposes a typology based on egocentric cocitation networks that displays a quadruple structuring (before and after publication): polarization, clusterization, atomization, and attrition. It shows that the academic capital of the authors and the intellectual resources of their research are key factors of these destinies, as are the social relations between the authors concerned. The circumstances of the publishing are also correlated with the structuring of the egocentric cocitation networks, showing how socially embedded they are. Finally, the article discusses the contribution of these original networks to the analyze of scientific production and its dynamics.
    Date
    21. 3.2023 19:22:14
  5. ¬Der Student aus dem Computer (2023) 0.03
    0.030135395 = product of:
      0.12054158 = sum of:
        0.082613975 = weight(_text_:computer in 1079) [ClassicSimilarity], result of:
          0.082613975 = score(doc=1079,freq=2.0), product of:
            0.1461475 = queryWeight, product of:
              3.6545093 = idf(docFreq=3109, maxDocs=44218)
              0.039991006 = queryNorm
            0.56527805 = fieldWeight in 1079, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.6545093 = idf(docFreq=3109, maxDocs=44218)
              0.109375 = fieldNorm(doc=1079)
        0.037927605 = product of:
          0.07585521 = sum of:
            0.07585521 = weight(_text_:22 in 1079) [ClassicSimilarity], result of:
              0.07585521 = score(doc=1079,freq=2.0), product of:
                0.1400417 = queryWeight, product of:
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.039991006 = queryNorm
                0.5416616 = fieldWeight in 1079, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.109375 = fieldNorm(doc=1079)
          0.5 = coord(1/2)
      0.25 = coord(2/8)
    
    Date
    27. 1.2023 16:22:55
  6. He, C.; Wu, J.; Zhang, Q.: Proximity-aware research leadership recommendation in research collaboration via deep neural networks (2022) 0.03
    0.028172642 = product of:
      0.11269057 = sum of:
        0.09797173 = weight(_text_:network in 446) [ClassicSimilarity], result of:
          0.09797173 = score(doc=446,freq=10.0), product of:
            0.17809492 = queryWeight, product of:
              4.4533744 = idf(docFreq=1398, maxDocs=44218)
              0.039991006 = queryNorm
            0.5501096 = fieldWeight in 446, product of:
              3.1622777 = tf(freq=10.0), with freq of:
                10.0 = termFreq=10.0
              4.4533744 = idf(docFreq=1398, maxDocs=44218)
              0.0390625 = fieldNorm(doc=446)
        0.014718837 = product of:
          0.029437674 = sum of:
            0.029437674 = weight(_text_:resources in 446) [ClassicSimilarity], result of:
              0.029437674 = score(doc=446,freq=2.0), product of:
                0.14598069 = queryWeight, product of:
                  3.650338 = idf(docFreq=3122, maxDocs=44218)
                  0.039991006 = queryNorm
                0.20165458 = fieldWeight in 446, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  3.650338 = idf(docFreq=3122, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=446)
          0.5 = coord(1/2)
      0.25 = coord(2/8)
    
    Abstract
    Collaborator recommendation is of great significance for facilitating research collaboration. Proximities have been demonstrated to be significant factors and determinants of research collaboration. Research leadership is associated with not only the capability to integrate resources to launch and sustain the research project but also the production and academic impact of the collaboration team. However, existing studies mainly focus on social or cognitive proximity, failing to integrate critical proximities comprehensively. Besides, existing studies focus on recommending relationships among all the coauthors, ignoring leadership in research collaboration. In this article, we propose a proximity-aware research leadership recommendation (PRLR) model to systematically integrate critical node attribute information (critical proximities) and network features to conduct research leadership recommendation by predicting the directed links in the research leadership network. PRLR integrates cognitive, geographical, and institutional proximity as node attribute information and constructs a leadership-aware coauthorship network to preserve the research leadership information. PRLR learns the node attribute information, the local network features, and the global network features with an autoencoder model, a joint probability constraint, and an attribute-aware skip-gram model, respectively. Extensive experiments and ablation studies have been conducted, demonstrating that PRLR significantly outperforms the state-of-the-art collaborator recommendation models in research leadership recommendation.
  7. Information : a historical companion (2021) 0.03
    0.027346224 = product of:
      0.109384894 = sum of:
        0.09273243 = weight(_text_:europe in 492) [ClassicSimilarity], result of:
          0.09273243 = score(doc=492,freq=4.0), product of:
            0.24358861 = queryWeight, product of:
              6.091085 = idf(docFreq=271, maxDocs=44218)
              0.039991006 = queryNorm
            0.3806928 = fieldWeight in 492, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              6.091085 = idf(docFreq=271, maxDocs=44218)
              0.03125 = fieldNorm(doc=492)
        0.016652463 = product of:
          0.033304926 = sum of:
            0.033304926 = weight(_text_:resources in 492) [ClassicSimilarity], result of:
              0.033304926 = score(doc=492,freq=4.0), product of:
                0.14598069 = queryWeight, product of:
                  3.650338 = idf(docFreq=3122, maxDocs=44218)
                  0.039991006 = queryNorm
                0.22814612 = fieldWeight in 492, product of:
                  2.0 = tf(freq=4.0), with freq of:
                    4.0 = termFreq=4.0
                  3.650338 = idf(docFreq=3122, maxDocs=44218)
                  0.03125 = fieldNorm(doc=492)
          0.5 = coord(1/2)
      0.25 = coord(2/8)
    
    Abstract
    Written by an international team of experts (including Jeremy Adelman, Lorraine Daston, Devin Fitzgerald, John-Paul Ghobrial, Lisa Gitelman, Earle Havens, Randolph C. Head, Niv Horesh, Sarah Igo, Richard R. John, Lauren Kassell, Pamela Long, Erin McGuirl, David McKitterick, Elias Muhanna, Thomas S. Mullaney, Carla Nappi, Craig Robertson, Daniel Rosenberg, Neil Safier, Haun Saussy, Will Slauter, Jacob Soll, Heidi Tworek, Siva Vaidhyanathan, Alexandra Walsham), the book's inspired and original long- and short-form contributions reconstruct the rise of human approaches to creating, managing, and sharing facts and knowledge. Thirteen full-length chapters discuss the role of information in pivotal epochs and regions, with chief emphasis on Europe and North America, but also substantive treatment of other parts of the world as well as current global interconnections. More than 100 alphabetical entries follow, focusing on specific tools, methods, and concepts?from ancient coins to the office memo, and censorship to plagiarism. The result is a wide-ranging, deeply immersive collection that will appeal to anyone drawn to the story behind our modern mania for an informed existence.
    Content
    Cover -- Contents -- Introduction -- Alphabetical List of Entries -- Thematic List of Entries -- Contributors -- PART ONE -- 1. Premodern Regimes and Practices -- 2. Realms of Information in the Medieval Islamic World -- 3. Information in Early Modern East Asia -- 4. Information in Early Modern Europe -- 5. Networks and the Making of a Connected World in the Sixteenth Century -- 6. Records, Secretaries, and the European Information State, circa 1400-1700 -- 7. Periodicals and the Commercialization of Information in the Early Modern Era -- 8. Documents, Empire, and Capitalism in the Nineteenth Century -- 9. Nineteenth-Century Media Technologies -- 10. Networking: Information Circles the Modern World -- 11. Publicity, Propaganda, and Public Opinion: From the Titanic Disaster to the Hungarian Uprising -- 12. Communication, Computation, and Information -- 13. Search -- PART TWO -- Alphabetical Entries -- Glossary -- Index.
    LCSH
    Information resources / History
    Subject
    Information resources / History
  8. Marcondes, C.H.: Towards a vocabulary to implement culturally relevant relationships between digital collections in heritage institutions (2020) 0.03
    0.025555706 = product of:
      0.10222282 = sum of:
        0.08867725 = weight(_text_:property in 5757) [ClassicSimilarity], result of:
          0.08867725 = score(doc=5757,freq=2.0), product of:
            0.25336683 = queryWeight, product of:
              6.335595 = idf(docFreq=212, maxDocs=44218)
              0.039991006 = queryNorm
            0.3499955 = fieldWeight in 5757, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              6.335595 = idf(docFreq=212, maxDocs=44218)
              0.0390625 = fieldNorm(doc=5757)
        0.013545574 = product of:
          0.027091147 = sum of:
            0.027091147 = weight(_text_:22 in 5757) [ClassicSimilarity], result of:
              0.027091147 = score(doc=5757,freq=2.0), product of:
                0.1400417 = queryWeight, product of:
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.039991006 = queryNorm
                0.19345059 = fieldWeight in 5757, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=5757)
          0.5 = coord(1/2)
      0.25 = coord(2/8)
    
    Abstract
    Cultural heritage institutions are publishing their digital collections over the web as LOD. This is is a new step in the patrimonialization and curatorial processes developed by such institutions. Many of these collections are thematically superimposed and complementary. Frequently, objects in these collections present culturally relevant relationships, such as a book about a painting, or a draft or sketch of a famous painting, etc. LOD technology enables such heritage records to be interlinked, achieving interoperability and adding value to digital collections, thus empowering heritage institutions. An aim of this research is characterizing such culturally relevant relationships and organizing them in a vocabulary. Use cases or examples of relationships between objects suggested by curators or mentioned in literature and in the conceptual models as FRBR/LRM, CIDOC CRM and RiC-CM, were collected and used as examples or inspiration of cultural relevant relationships. Relationships identified are collated and compared for identifying those with the same or similar meaning, synthesized and normalized. A set of thirty-three culturally relevant relationships are identified and formalized as a LOD property vocabulary to be used by digital curators to interlink digital collections. The results presented are provisional and a starting point to be discussed, tested, and enhanced.
    Date
    4. 3.2020 14:22:41
  9. Zhang, Y.; Wu, M.; Zhang, G.; Lu, J.: Stepping beyond your comfort zone : diffusion-based network analytics for knowledge trajectory recommendation (2023) 0.03
    0.025293538 = product of:
      0.10117415 = sum of:
        0.08762858 = weight(_text_:network in 994) [ClassicSimilarity], result of:
          0.08762858 = score(doc=994,freq=8.0), product of:
            0.17809492 = queryWeight, product of:
              4.4533744 = idf(docFreq=1398, maxDocs=44218)
              0.039991006 = queryNorm
            0.492033 = fieldWeight in 994, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              4.4533744 = idf(docFreq=1398, maxDocs=44218)
              0.0390625 = fieldNorm(doc=994)
        0.013545574 = product of:
          0.027091147 = sum of:
            0.027091147 = weight(_text_:22 in 994) [ClassicSimilarity], result of:
              0.027091147 = score(doc=994,freq=2.0), product of:
                0.1400417 = queryWeight, product of:
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.039991006 = queryNorm
                0.19345059 = fieldWeight in 994, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=994)
          0.5 = coord(1/2)
      0.25 = coord(2/8)
    
    Abstract
    Predicting a researcher's knowledge trajectories beyond their current foci can leverage potential inter-/cross-/multi-disciplinary interactions to achieve exploratory innovation. In this study, we present a method of diffusion-based network analytics for knowledge trajectory recommendation. The method begins by constructing a heterogeneous bibliometric network consisting of a co-topic layer and a co-authorship layer. A novel link prediction approach with a diffusion strategy is then used to capture the interactions between social elements (e.g., collaboration) and knowledge elements (e.g., technological similarity) in the process of exploratory innovation. This diffusion strategy differentiates the interactions occurring among homogeneous and heterogeneous nodes in the heterogeneous bibliometric network and weights the strengths of these interactions. Two sets of experiments-one with a local dataset and the other with a global dataset-demonstrate that the proposed method is prior to 10 selected baselines in link prediction, recommender systems, and upstream graph representation learning. A case study recommending knowledge trajectories of information scientists with topical hierarchy and explainable mediators reveals the proposed method's reliability and potential practical uses in broad scenarios.
    Date
    22. 6.2023 18:07:12
  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) 0.02
    0.022358537 = product of:
      0.08943415 = sum of:
        0.075888574 = weight(_text_:network in 263) [ClassicSimilarity], result of:
          0.075888574 = score(doc=263,freq=6.0), product of:
            0.17809492 = queryWeight, product of:
              4.4533744 = idf(docFreq=1398, maxDocs=44218)
              0.039991006 = queryNorm
            0.42611307 = fieldWeight in 263, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              4.4533744 = idf(docFreq=1398, maxDocs=44218)
              0.0390625 = fieldNorm(doc=263)
        0.013545574 = product of:
          0.027091147 = sum of:
            0.027091147 = weight(_text_:22 in 263) [ClassicSimilarity], result of:
              0.027091147 = score(doc=263,freq=2.0), product of:
                0.1400417 = queryWeight, product of:
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.039991006 = queryNorm
                0.19345059 = fieldWeight in 263, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=263)
          0.5 = coord(1/2)
      0.25 = coord(2/8)
    
    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. Yu, C.; Xue, H.; An, L.; Li, G.: ¬A lightweight semantic-enhanced interactive network for efficient short-text matching (2023) 0.02
    0.022358537 = product of:
      0.08943415 = sum of:
        0.075888574 = weight(_text_:network in 890) [ClassicSimilarity], result of:
          0.075888574 = score(doc=890,freq=6.0), product of:
            0.17809492 = queryWeight, product of:
              4.4533744 = idf(docFreq=1398, maxDocs=44218)
              0.039991006 = queryNorm
            0.42611307 = fieldWeight in 890, product of:
              2.4494898 = tf(freq=6.0), with freq of:
                6.0 = termFreq=6.0
              4.4533744 = idf(docFreq=1398, maxDocs=44218)
              0.0390625 = fieldNorm(doc=890)
        0.013545574 = product of:
          0.027091147 = sum of:
            0.027091147 = weight(_text_:22 in 890) [ClassicSimilarity], result of:
              0.027091147 = score(doc=890,freq=2.0), product of:
                0.1400417 = queryWeight, product of:
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.039991006 = queryNorm
                0.19345059 = fieldWeight in 890, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=890)
          0.5 = coord(1/2)
      0.25 = coord(2/8)
    
    Abstract
    Knowledge-enhanced short-text matching has been a significant task attracting much attention in recent years. However, the existing approaches cannot effectively balance effect and efficiency. Effective models usually consist of complex network structures leading to slow inference speed and the difficulties of applications in actual practice. In addition, most knowledge-enhanced models try to link the mentions in the text to the entities of the knowledge graphs-the difficulties of entity linking decrease the generalizability among different datasets. To address these problems, we propose a lightweight Semantic-Enhanced Interactive Network (SEIN) model for efficient short-text matching. Unlike most current research, SEIN employs an unsupervised method to select WordNet's most appropriate paraphrase description as the external semantic knowledge. It focuses on integrating semantic information and interactive information of text while simplifying the structure of other modules. We conduct intensive experiments on four real-world datasets, that is, Quora, Twitter-URL, SciTail, and SICK-E. Compared with state-of-the-art methods, SEIN achieves the best performance on most datasets. The experimental results proved that introducing external knowledge could effectively improve the performance of the short-text matching models. The research sheds light on the role of lightweight models in leveraging external knowledge to improve the effect of short-text matching.
    Date
    22. 1.2023 19:05:27
  12. Singh, V.K.; Chayko, M.; Inamdar, R.; Floegel, D.: Female librarians and male computer programmers? : gender bias in occupational images on digital media platforms (2020) 0.02
    0.021385163 = product of:
      0.08554065 = sum of:
        0.041726362 = weight(_text_:computer in 6) [ClassicSimilarity], result of:
          0.041726362 = score(doc=6,freq=4.0), product of:
            0.1461475 = queryWeight, product of:
              3.6545093 = idf(docFreq=3109, maxDocs=44218)
              0.039991006 = queryNorm
            0.28550854 = fieldWeight in 6, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.6545093 = idf(docFreq=3109, maxDocs=44218)
              0.0390625 = fieldNorm(doc=6)
        0.04381429 = weight(_text_:network in 6) [ClassicSimilarity], result of:
          0.04381429 = score(doc=6,freq=2.0), product of:
            0.17809492 = queryWeight, product of:
              4.4533744 = idf(docFreq=1398, maxDocs=44218)
              0.039991006 = queryNorm
            0.2460165 = fieldWeight in 6, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.4533744 = idf(docFreq=1398, maxDocs=44218)
              0.0390625 = fieldNorm(doc=6)
      0.25 = coord(2/8)
    
    Abstract
    Media platforms, technological systems, and search engines act as conduits and gatekeepers for all kinds of information. They often influence, reflect, and reinforce gender stereotypes, including those that represent occupations. This study examines the prevalence of gender stereotypes on digital media platforms and considers how human efforts to create and curate messages directly may impact these stereotypes. While gender stereotyping in social media and algorithms has received some examination in the recent literature, its prevalence in different types of platforms (for example, wiki vs. news vs. social network) and under differing conditions (for example, degrees of human- and machine-led content creation and curation) has yet to be studied. This research explores the extent to which stereotypes of certain strongly gendered professions (librarian, nurse, computer programmer, civil engineer) persist and may vary across digital platforms (Twitter, the New York Times online, Wikipedia, and Shutterstock). The results suggest that gender stereotypes are most likely to be challenged when human beings act directly to create and curate content in digital platforms, and that highly algorithmic approaches for curation showed little inclination towards breaking stereotypes. Implications for the more inclusive design and use of digital media platforms, particularly with regard to mediated occupational messaging, are discussed.
  13. Rieder, B.: Engines of order : a mechanology of algorithmic techniques (2020) 0.02
    0.021385163 = product of:
      0.08554065 = sum of:
        0.041726362 = weight(_text_:computer in 315) [ClassicSimilarity], result of:
          0.041726362 = score(doc=315,freq=4.0), product of:
            0.1461475 = queryWeight, product of:
              3.6545093 = idf(docFreq=3109, maxDocs=44218)
              0.039991006 = queryNorm
            0.28550854 = fieldWeight in 315, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.6545093 = idf(docFreq=3109, maxDocs=44218)
              0.0390625 = fieldNorm(doc=315)
        0.04381429 = weight(_text_:network in 315) [ClassicSimilarity], result of:
          0.04381429 = score(doc=315,freq=2.0), product of:
            0.17809492 = queryWeight, product of:
              4.4533744 = idf(docFreq=1398, maxDocs=44218)
              0.039991006 = queryNorm
            0.2460165 = fieldWeight in 315, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.4533744 = idf(docFreq=1398, maxDocs=44218)
              0.0390625 = fieldNorm(doc=315)
      0.25 = coord(2/8)
    
    Abstract
    Software has become a key component of contemporary life and algorithmic techniques that rank, classify, or recommend anything that fits into digital form are everywhere. This book approaches the field of information ordering conceptually as well as historically. Building on the philosophy of Gilbert Simondon and the cultural techniques tradition, it first examines the constructive and cumulative character of software and shows how software-making constantly draws on large reservoirs of existing knowledge and techniques. It then reconstructs the historical trajectories of a series of algorithmic techniques that have indeed become the building blocks for contemporary practices of ordering. Developed in opposition to centuries of library tradition, coordinate indexing, text processing, machine learning, and network algorithms instantiate dynamic, perspectivist, and interested forms of arranging information, ideas, or people. Embedded in technical infrastructures and economic logics, these techniques have become engines of order that transform the spaces they act upon.
    LCSH
    Algorithms ; Computer software
    Subject
    Algorithms ; Computer software
  14. Isaac, A.; Raemy, J.A.; Meijers, E.; Valk, S. De; Freire, N.: Metadata aggregation via linked data : results of the Europeana Common Culture project (2020) 0.02
    0.01938896 = product of:
      0.07755584 = sum of:
        0.052577145 = weight(_text_:network in 39) [ClassicSimilarity], result of:
          0.052577145 = score(doc=39,freq=2.0), product of:
            0.17809492 = queryWeight, product of:
              4.4533744 = idf(docFreq=1398, maxDocs=44218)
              0.039991006 = queryNorm
            0.29521978 = fieldWeight in 39, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.4533744 = idf(docFreq=1398, maxDocs=44218)
              0.046875 = fieldNorm(doc=39)
        0.024978695 = product of:
          0.04995739 = sum of:
            0.04995739 = weight(_text_:resources in 39) [ClassicSimilarity], result of:
              0.04995739 = score(doc=39,freq=4.0), product of:
                0.14598069 = queryWeight, product of:
                  3.650338 = idf(docFreq=3122, maxDocs=44218)
                  0.039991006 = queryNorm
                0.34221917 = fieldWeight in 39, product of:
                  2.0 = tf(freq=4.0), with freq of:
                    4.0 = termFreq=4.0
                  3.650338 = idf(docFreq=3122, maxDocs=44218)
                  0.046875 = fieldNorm(doc=39)
          0.5 = coord(1/2)
      0.25 = coord(2/8)
    
    Abstract
    Digital cultural heritage resources are widely available on the web through the digital libraries of heritage institutions. To address the difficulties of discoverability in cultural heritage, the common practice is metadata aggregation, where centralized efforts like Europeana facilitate discoverability by collecting the resources' metadata. We present the results of the linked data aggregation task conducted within the Europeana Common Culture project, which attempted an innovative approach to aggregation based on linked data made available by cultural heritage institutions. This task ran for one year with participation of eleven organizations, involving the three member roles of the Europeana network: data providers, intermediary aggregators, and the central aggregation hub, Europeana. We report on the challenges that were faced by data providers, the standards and specifications applied, and the resulting aggregated metadata.
  15. Hottenrott, H.; Rose, M.E.; Lawson, C.: ¬The rise of multiple institutional affiliations in academia (2021) 0.02
    0.018877085 = product of:
      0.07550834 = sum of:
        0.061962765 = weight(_text_:network in 313) [ClassicSimilarity], result of:
          0.061962765 = score(doc=313,freq=4.0), product of:
            0.17809492 = queryWeight, product of:
              4.4533744 = idf(docFreq=1398, maxDocs=44218)
              0.039991006 = queryNorm
            0.34791988 = fieldWeight in 313, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              4.4533744 = idf(docFreq=1398, maxDocs=44218)
              0.0390625 = fieldNorm(doc=313)
        0.013545574 = product of:
          0.027091147 = sum of:
            0.027091147 = weight(_text_:22 in 313) [ClassicSimilarity], result of:
              0.027091147 = score(doc=313,freq=2.0), product of:
                0.1400417 = queryWeight, product of:
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.039991006 = queryNorm
                0.19345059 = fieldWeight in 313, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=313)
          0.5 = coord(1/2)
      0.25 = coord(2/8)
    
    Abstract
    This study provides the first systematic, international, large-scale evidence on the extent and nature of multiple institutional affiliations on journal publications. Studying more than 15 million authors and 22 million articles from 40 countries we document that: In 2019, almost one in three articles was (co-)authored by authors with multiple affiliations and the share of authors with multiple affiliations increased from around 10% to 16% since 1996. The growth of multiple affiliations is prevalent in all fields and it is stronger in high impact journals. About 60% of multiple affiliations are between institutions from within the academic sector. International co-affiliations, which account for about a quarter of multiple affiliations, most often involve institutions from the United States, China, Germany and the United Kingdom, suggesting a core-periphery network. Network analysis also reveals a number communities of countries that are more likely to share affiliations. We discuss potential causes and show that the timing of the rise in multiple affiliations can be linked to the introduction of more competitive funding structures such as "excellence initiatives" in a number of countries. We discuss implications for science and science policy.
  16. Vierkant, P.: Entwurf des DataCite-Metadatenschemas 4.5 offen für Kommentierung (2022) 0.02
    0.018811285 = product of:
      0.15049028 = sum of:
        0.15049028 = weight(_text_:property in 672) [ClassicSimilarity], result of:
          0.15049028 = score(doc=672,freq=4.0), product of:
            0.25336683 = queryWeight, product of:
              6.335595 = idf(docFreq=212, maxDocs=44218)
              0.039991006 = queryNorm
            0.5939621 = fieldWeight in 672, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              6.335595 = idf(docFreq=212, maxDocs=44218)
              0.046875 = fieldNorm(doc=672)
      0.125 = coord(1/8)
    
    Abstract
    "In den letzten anderthalb Jahren hat die DataCite Metadata Working Group an Änderungen für die neue Version des DataCite Metadatenschemas gearbeitet, um die sich entwickelnden neuen Anwendungsfälle für DataCite DOIs zu unterstützen. Diese vorgeschlagenen Aktualisierungen sind eine Reaktion auf Anfragen von Mitgliedern der DataCite-Community. Wir möchten sicherstellen, dass diese Änderungen funktionieren, d. h. dass sie die Probleme lösen, die sie lösen sollen. Zum ersten Mal überhaupt stellen wir deshalb einen RFC-Entwurf zur Kommentierung bereit, bevor wir die nächste Version (4.5) des Metadatenschemas veröffentlichen. Dieser Entwurf beinhaltet: * Unterstützung für Instrumente * Unterstützung von pre-registrations und registration reports * Unterstützung für Verlagsidentifikatoren * Neues Distribution property * Erläuterungen zum RelatedItem property * Aktualisierte PhysicalObject-Definition Details zur Rückmeldung sind in dem Request for Comments enthalten: https://docs.google.com/document/d/1UyQQwtjnu-4_4zXE4TFZ74-mjLZI3NkEf8RrF0WeOdI/edit Eine Kopie des Vorschlags im PDF-Format ist ebenfalls verfügbar: https://datacite.org/documents/DataCite_Metadata_Schema_4.5_RFC.pdf Weitere Informationen finden Sie im DataCite Blog: https://doi.org/10.5438/q34f-c696."
  17. Rieger, F.: Lügende Computer (2023) 0.02
    0.017220227 = product of:
      0.06888091 = sum of:
        0.04720799 = weight(_text_:computer in 912) [ClassicSimilarity], result of:
          0.04720799 = score(doc=912,freq=2.0), product of:
            0.1461475 = queryWeight, product of:
              3.6545093 = idf(docFreq=3109, maxDocs=44218)
              0.039991006 = queryNorm
            0.32301605 = fieldWeight in 912, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.6545093 = idf(docFreq=3109, maxDocs=44218)
              0.0625 = fieldNorm(doc=912)
        0.021672918 = product of:
          0.043345835 = sum of:
            0.043345835 = weight(_text_:22 in 912) [ClassicSimilarity], result of:
              0.043345835 = score(doc=912,freq=2.0), product of:
                0.1400417 = queryWeight, product of:
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.039991006 = queryNorm
                0.30952093 = fieldWeight in 912, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.0625 = fieldNorm(doc=912)
          0.5 = coord(1/2)
      0.25 = coord(2/8)
    
    Date
    16. 3.2023 19:22:55
  18. Lorentzen, D.G.: Bridging polarised Twitter discussions : the interactions of the users in the middle (2021) 0.02
    0.017207958 = product of:
      0.06883183 = sum of:
        0.052577145 = weight(_text_:network in 182) [ClassicSimilarity], result of:
          0.052577145 = score(doc=182,freq=2.0), product of:
            0.17809492 = queryWeight, product of:
              4.4533744 = idf(docFreq=1398, maxDocs=44218)
              0.039991006 = queryNorm
            0.29521978 = fieldWeight in 182, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.4533744 = idf(docFreq=1398, maxDocs=44218)
              0.046875 = fieldNorm(doc=182)
        0.016254688 = product of:
          0.032509375 = sum of:
            0.032509375 = weight(_text_:22 in 182) [ClassicSimilarity], result of:
              0.032509375 = score(doc=182,freq=2.0), product of:
                0.1400417 = queryWeight, product of:
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.039991006 = queryNorm
                0.23214069 = fieldWeight in 182, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.046875 = fieldNorm(doc=182)
          0.5 = coord(1/2)
      0.25 = coord(2/8)
    
    Abstract
    Purpose The purpose of the paper is to analyse the interactions of bridging users in Twitter discussions about vaccination. Design/methodology/approach Conversational threads were collected through filtering the Twitter stream using keywords and the most active participants in the conversations. Following data collection and anonymisation of tweets and user profiles, a retweet network was created to find users bridging the main clusters. Four conversations were selected, ranging from 456 to 1,983 tweets long, and then analysed through content analysis. Findings Although different opinions met in the discussions, a consensus was rarely built. Many sub-threads involved insults and criticism, and participants seemed not interested in shifting their positions. However, examples of reasoned discussions were also found. Originality/value The study analyses conversations on Twitter, which is rarely studied. The focus on the interactions of bridging users adds to the uniqueness of the paper.
    Date
    20. 1.2015 18:30:22
  19. Park, Y.J.: ¬A socio-technological model of search information divide in US cities (2021) 0.02
    0.017207958 = product of:
      0.06883183 = sum of:
        0.052577145 = weight(_text_:network in 184) [ClassicSimilarity], result of:
          0.052577145 = score(doc=184,freq=2.0), product of:
            0.17809492 = queryWeight, product of:
              4.4533744 = idf(docFreq=1398, maxDocs=44218)
              0.039991006 = queryNorm
            0.29521978 = fieldWeight in 184, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.4533744 = idf(docFreq=1398, maxDocs=44218)
              0.046875 = fieldNorm(doc=184)
        0.016254688 = product of:
          0.032509375 = sum of:
            0.032509375 = weight(_text_:22 in 184) [ClassicSimilarity], result of:
              0.032509375 = score(doc=184,freq=2.0), product of:
                0.1400417 = queryWeight, product of:
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.039991006 = queryNorm
                0.23214069 = fieldWeight in 184, product of:
                  1.4142135 = tf(freq=2.0), with freq of:
                    2.0 = termFreq=2.0
                  3.5018296 = idf(docFreq=3622, maxDocs=44218)
                  0.046875 = fieldNorm(doc=184)
          0.5 = coord(1/2)
      0.25 = coord(2/8)
    
    Abstract
    Purpose The purpose of the paper is to analyse the interactions of bridging users in Twitter discussions about vaccination. Design/methodology/approach Conversational threads were collected through filtering the Twitter stream using keywords and the most active participants in the conversations. Following data collection and anonymisation of tweets and user profiles, a retweet network was created to find users bridging the main clusters. Four conversations were selected, ranging from 456 to 1,983 tweets long, and then analysed through content analysis. Findings Although different opinions met in the discussions, a consensus was rarely built. Many sub-threads involved insults and criticism, and participants seemed not interested in shifting their positions. However, examples of reasoned discussions were also found. Originality/value The study analyses conversations on Twitter, which is rarely studied. The focus on the interactions of bridging users adds to the uniqueness of the paper.
    Date
    20. 1.2015 18:30:22
  20. 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) 0.02
    0.015635485 = product of:
      0.06254194 = sum of:
        0.041726362 = weight(_text_:computer in 663) [ClassicSimilarity], result of:
          0.041726362 = score(doc=663,freq=4.0), product of:
            0.1461475 = queryWeight, product of:
              3.6545093 = idf(docFreq=3109, maxDocs=44218)
              0.039991006 = queryNorm
            0.28550854 = fieldWeight in 663, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              3.6545093 = idf(docFreq=3109, maxDocs=44218)
              0.0390625 = fieldNorm(doc=663)
        0.02081558 = product of:
          0.04163116 = sum of:
            0.04163116 = weight(_text_:resources in 663) [ClassicSimilarity], result of:
              0.04163116 = score(doc=663,freq=4.0), product of:
                0.14598069 = queryWeight, product of:
                  3.650338 = idf(docFreq=3122, maxDocs=44218)
                  0.039991006 = queryNorm
                0.28518265 = fieldWeight in 663, product of:
                  2.0 = tf(freq=4.0), with freq of:
                    4.0 = termFreq=4.0
                  3.650338 = idf(docFreq=3122, maxDocs=44218)
                  0.0390625 = fieldNorm(doc=663)
          0.5 = coord(1/2)
      0.25 = coord(2/8)
    
    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.

Languages

  • e 213
  • d 41

Types

  • a 233
  • el 31
  • m 13
  • p 4
  • s 1
  • x 1
  • More… Less…

Subjects