@inproceedings{gui-2025-entropy,
    title = "From Entropy to Generalizability: Strengthening Automated Essay Scoring Reliability and Sustainability",
    author = "Gui, Yi",
    editor = "Wilson, Joshua  and
      Ormerod, Christopher  and
      Beiting Parrish, Magdalen",
    booktitle = "Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Full Papers",
    month = oct,
    year = "2025",
    address = "Wyndham Grand Pittsburgh, Downtown, Pittsburgh, Pennsylvania, United States",
    publisher = "National Council on Measurement in Education (NCME)",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.aimecon-main.34/",
    pages = "312--328",
    ISBN = "979-8-218-84228-4",
    abstract = "Generalizability Theory with entropy-derived stratification optimized automated essay scoring reliability. A G-study decomposed variance across 14 encoders and 3 seeds; D-studies identified minimal ensembles achieving G {\ensuremath{\geq}} 0.85. A hybrid of one medium and one small encoder with two seeds maximized dependability per compute cost. Stratification ensured uniform precision across"
}