Contextual Benchmarking

Contextual Benchmarking is a scholarly evaluation methodology that, as a subtype of benchmarking practices in academic research, specifically involves the systematic comparison of models, methods, or systems against context-sensitive criteria or baselines derived from the unique characteristics of a given domain, dataset, or problem setting, thereby distinguishing it from generic or standardized benchmarking approaches by its intrinsic adaptation to situational and environmental contingencies.

3

Publications

113

Citations

37

Authors

30

Institutions

Publications per year

2017–2026

2

Authors

37

  1. SH
    Sehoon Ha

    Georgia Institute of Technology, United States

    2 publications · 77 citations

    2

    h-index

  2. AV
    Ada V. Taylor

    Carnegie Mellon University, United States

    2 publications · 77 citations

    2

    h-index

  3. AT
    Alexander Toshev

    Google (United States), United States

    2 publications · 77 citations

    2

    h-index

  4. PT
    Phani Teja Singamaneni

    Centre National de la Recherche Scientifique, France

    2 publications · 77 citations

    2

    h-index

  5. RM
    Roberto Martín-Martín

    Stanford University, United States

    2 publications · 77 citations

    2

    h-index

1–5 of 37

Venues

  1. ACM Transactions on Human-Robot Interaction

    THRI · 1 publications · 62 citations

    1

    h-index

  2. Government Information Quarterly

    1 publications · 36 citations

    1

    h-index

  3. Aston Publications Explorer (Aston University)

    Repository · 1 publications · 15 citations

    1

    h-index

1–3