Historical Meme Profiles and Legal Language Models for Appellate Vote Alignment Prediction

Predicting disagreement on appellate panels is a useful problem for empirical legal studies because dissent is rare, institutionally important, and often tied to persistent differences in judicial style and precedent use. Prior English-language work has shown that structured case features, seating patterns, and citation-derived phrase statistics can help predict vote alignment in the U.S. Courts of Appeals, while recent legal NLP work has introduced domain-specific language models and stronger long-document encoders. This paper proposes a modest empirical synthesis of those lines of work. The key idea is to retain the meme-scoring intuition of Verma et al., but compute meme and semantic profiles only from each judge's historical opinions prior to the target case. Pairwise agreement is then predicted from structured panel-history features, historical meme overlap, and legal language-model similarity. A public-data pilot on streamed Caselaw Access Project opinions evaluates dissent-derived disagreement labels under a chronological split. On 1,764 held-out judge-pair examples with 173 disagreement positives, the hybrid model reaches macro F1 of 0.588, disagreement-class F1 of 0.335, average precision of 0.255, and ROC AUC of 0.772. These results are not definitive vote-label evidence, but they suggest that historical judicial profiles contain measurable ranking signal for future panel disagreement.