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Kim, J.: Describing and predicting information-seeking behavior on the Web (2009)
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- Date
- 22. 3.2009 18:54:15
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Yakel, E.; Kim, J.: Adoption and diffusion of Encoded Archival Description (2005)
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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.
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Kim, J.: Scale-free collaboration networks : an author name disambiguation perspective (2019)
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- Abstract
- Several studies have found that collaboration networks are scale-free, proposing that such networks can be modeled by specific network evolution mechanisms like preferential attachment. This study argues that collaboration networks can look more or less scale-free depending on the methods for resolving author name ambiguity in bibliographic data. Analyzing networks constructed from multiple datasets containing 3.4 M ~ 9.6 M publication records, this study shows that collaboration networks in which author names are disambiguated by the commonly used heuristic, i.e., forename-initial-based name matching, tend to produce degree distributions better fitted to power-law slopes with the typical scaling parameter (2 < a < 3) than networks disambiguated by more accurate algorithm-based methods. Such tendency is observed across collaboration networks generated under various conditions such as cumulative years, 5- and 1-year sliding windows, and random sampling, and through simulation, found to arise due mainly to artefactual entities created by inaccurate disambiguation. This cautionary study calls for special attention from scholars analyzing network data in which entities such as people, organization, and gene can be merged or split by improper disambiguation.
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Kim, J.; Kim, J.; Owen-Smith, J.: Ethnicity-based name partitioning for author name disambiguation using supervised machine learning (2021)
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Kang, I.-S.; Na, S.-H.; Kim, J.; Lee, J.-H.: Cluster-based patent retrieval (2007)
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Kim, J.; Thomas, P.; Sankaranarayana, R.; Gedeon, T.; Yoon, H.-J.: Eye-tracking analysis of user behavior and performance in web search on large and small screens (2015)
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Kim, J.; Diesner, J.: Coauthorship networks : a directed network approach considering the order and number of coauthors (2015)
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Kim, J.; Diesner, J.: Distortive effects of initial-based name disambiguation on measurements of large-scale coauthorship networks (2016)
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Kim, J.; Thomas, P.; Sankaranarayana, R.; Gedeon, T.; Yoon, H.-J.: Understanding eye movements on mobile devices for better presentation of search results (2016)
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Walsh, J.A.; Cobb, P.J.; Fremery, W. de; Golub, K.; Keah, H.; Kim, J.; Kiplang'at, J.; Liu, Y.-H.; Mahony, S.; Oh, S.G.; Sula, C.A.; Underwood, T.; Wang, X.: Digital humanities in the iSchool (2022)
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Kim, J.: Faculty self-archiving : motivations and barriers (2010)
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Kim, J.: Author-based analysis of conference versus journal publication in computer science (2019)
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