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Author: search.filters.author.Chumthong, ThanathipHas files: No

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    A longitudinal examination of collaboration diversity among communication scholars: 1990–2023
    (2024-12-01)
    Xu, Shan
    ;
    Jitkajornwanich, Kulsawasd
    ;
    David, Prabu
    ;
    Park, Hye Jung
    ;
    Zhao, Yani
    This study examines racial diversity in co-authorship in articles published in communication journals and its association with citations accrued over time. We analyzed 76,217 publications from 73 communication journals, spanning from 1990 to 2023, with a focus on racial diversity in authorship as an indicator of collaboration diversity. Our results reveal that diversity is positively associated with the number of citations received, with this positive effect increasing over time. In addition, non-White lead authors collaborated more diversely, whereas White authors exhibited a faster increase in collaboration diversity over the years. Furthermore, the positive association between collaboration diversity and citations was more pronounced when the lead author was non-White than when White. Additional analyses show a concerning disparity: While non-White first authors are equally likely as their White counterparts to publish in top journals, they receive significantly fewer citations.
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    Leveraging Race Prediction Algorithms to Enhance Team Composition in Big Data Science Teams
    (2024-01-01)
    Chumthong, Thanathip
    ;
    Jitkajornwanich, Kulsawasd
    ;
    Kraishan, Obada
    ;
    Kee, Kerk F.
    ;
    Narabin, Akan
    As big data science projects scale in complexity, optimizing team composition has become vital for improving creativity, productivity, and project success. We explore the possibility of incorporating race prediction algorithms for enhancing racial diversity in team composition in big data science projects. This paper evaluates five race prediction algorithms - wru, ethnicolr, ethnicolr2, pyethnicity, and rethnicity - and then discuss their potential in supporting racially diverse team assembly in big data projects. Utilizing three datasets, we assess algorithm performance and applicability, emphasizing their role in building balanced teams that enhance agility, inclusivity, and bias mitigation. We present an actionable methodology for integrating demographic insights into team management. In addition, we propose ethical safeguards to ensure responsible race prediction use, recommending data privacy measures, aggregate-only data handling, and transparency in communication. We argue that when used within ethical constraints, race prediction can support robust team processes, reduce reliance on less diverse teams, and ultimately facilitate more creative and equitable big data project outcomes.