Automated Coding Systems

Automated Coding Systems is a methodological framework in data coding that employs algorithmic and machine-learning techniques to systematically categorize and annotate data without continuous human intervention, distinguishing it from manual or semi-automated approaches by its reliance on computational autonomy for scalable and consistent code application.

59

Publications

2.3K

Citations

207

Authors

92

Institutions

Publications per year

2017–2026

30

Authors

207

  1. PA
    Philip A. Schrodt

    University of Kansas, United States

    5 publications · 378 citations

    5

    h-index

  2. KF
    Ken Fujiwara

    Jichi Medical University, Japan

    2 publications · 62 citations

    2

    h-index

  3. JA
    Jeewani Anupama Ginige

    Western Sydney University, Australia

    2 publications · 65 citations

    2

    h-index

  4. CD
    Christopher D. Otmar

    University of California, Santa Barbara, United States

    2 publications · 62 citations

    2

    h-index

  5. CG
    Cees G. M. Snoek

    University of Amsterdam, Netherlands

    2 publications · 34 citations

    2

    h-index

1–5 of 207

Venues

  1. PLoS ONE

    3 publications · 59 citations

    3

    h-index

  2. International Interactions

    2 publications · 138 citations

    2

    h-index

  3. Social Science Computer Review

    SSCR · 2 publications · 170 citations

    2

    h-index

  4. The Annals of Occupational Hygiene

    1 publications · 35 citations

    1

    h-index

  5. Journal of Biomedical Informatics

    1 publications · 22 citations

    1

    h-index

1–5