Roll-call Vote Forecasting

Roll-Call Vote Forecasting is a computational method that predicts individual legislators' voting behavior on specific legislative roll-call votes using statistical or machine learning models based on contextual, behavioral, and institutional features.

4

Publications

931

Citations

6

Authors

2

Institutions

Publications per year

2017–2026

1

Authors

6

  1. RM
    Richard M. Yon

    University of Chicago, United States

    1 publications · 22 citations

    1

    h-index

  2. RS
    Richard S. Conley

    University of Florida, United States

    1 publications · 22 citations

    1

    h-index

  3. JE
    James E. Campbell

    University at Buffalo, State University of New York, United States

    1 publications · 29 citations

    1

    h-index

  4. AB
    Adam Bonica

    Stanford University, United States

    1 publications · 69 citations

    1

    h-index

  5. KT
    Keith T. Poole

    University of Georgia, United States

    1 publications · 811 citations

    1

    h-index

1–5 of 6

Institutions

2

  1. University of Florida

    Gainesville, United States

    1 h-index

    2 publications

    44 citations

  2. Stanford University

    Stanford, United States

    1 h-index

    1 publications

    69 citations

1–2 of 2

Venues

  1. American Journal of Political Science

    AJPS · 2 publications · 880 citations

    2

    h-index

  2. Presidential Studies Quarterly

    1 publications · 22 citations

    1

    h-index

  3. Journal of Political Marketing

    1 publications · 29 citations

    1

    h-index

1–3