Search (3 results, page 1 of 1)

  • × author_ss:"Kim, J."
  • × theme_ss:"Formalerschließung"
  1. Yakel, E.; Kim, J.: Adoption and diffusion of Encoded Archival Description (2005) 0.00 
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    Abstract
    In this article, findings from a study an the diffusion and adoption of Encoded Archival Description (EAD) within the U.S. archival community are reported. Using E. M. Rogers' (1995) theory of the diffusion of innovations as a theoretical framework, the authors surveyed 399 archives and manuscript repositories that sent participants to EAD workshops from 1993-2002. Their findings indicated that EAD diffusion and adoption are complex phenomena. While the diffusion pattern mirrored that of MAchine-Readable Cataloging (MARC), overall adoption was slow. Only 42% of the survey respondents utilized EAD in their descriptive programs. Critical factors inhibiting adoption include the small staff size of many repositories, the lack of standardization in archival descriptive practices, a multiplicity of existing archival access tools, insufficient institutional infrastructure, and difficulty in maintaining expertise.
  2. Kim, J.; Kim, J.; Owen-Smith, J.: Ethnicity-based name partitioning for author name disambiguation using supervised machine learning (2021) 0.00 
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    Abstract
    In several author name disambiguation studies, some ethnic name groups such as East Asian names are reported to be more difficult to disambiguate than others. This implies that disambiguation approaches might be improved if ethnic name groups are distinguished before disambiguation. We explore the potential of ethnic name partitioning by comparing performance of four machine learning algorithms trained and tested on the entire data or specifically on individual name groups. Results show that ethnicity-based name partitioning can substantially improve disambiguation performance because the individual models are better suited for their respective name group. The improvements occur across all ethnic name groups with different magnitudes. Performance gains in predicting matched name pairs outweigh losses in predicting nonmatched pairs. Feature (e.g., coauthor name) similarities of name pairs vary across ethnic name groups. Such differences may enable the development of ethnicity-specific feature weights to improve prediction for specific ethic name categories. These findings are observed for three labeled data with a natural distribution of problem sizes as well as one in which all ethnic name groups are controlled for the same sizes of ambiguous names. This study is expected to motive scholars to group author names based on ethnicity prior to disambiguation.
  3. Kim, J.; Diesner, J.: Distortive effects of initial-based name disambiguation on measurements of large-scale coauthorship networks (2016) 0.00 
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    Abstract
    Scholars have often relied on name initials to resolve name ambiguities in large-scale coauthorship network research. This approach bears the risk of incorrectly merging or splitting author identities. The use of initial-based disambiguation has been justified by the assumption that such errors would not affect research findings too much. This paper tests that assumption by analyzing coauthorship networks from five academic fields-biology, computer science, nanoscience, neuroscience, and physics-and an interdisciplinary journal, PNAS. Name instances in data sets of this study were disambiguated based on heuristics gained from previous algorithmic disambiguation solutions. We use disambiguated data as a proxy of ground-truth to test the performance of three types of initial-based disambiguation. Our results show that initial-based disambiguation can misrepresent statistical properties of coauthorship networks: It deflates the number of unique authors, number of components, average shortest paths, clustering coefficient, and assortativity, while it inflates average productivity, density, average coauthor number per author, and largest component size. Also, on average, more than half of top 10 productive or collaborative authors drop off the lists. Asian names were found to account for the majority of misidentification by initial-based disambiguation due to their common surname and given name initials.

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