Abstract Summary
This work bridges information retrieval and cultural analytics to support equitable access to historical knowledge. Using the British Library¡¯s BL19 digital collection (more than $35,000$ works from $1700-1899$), we construct a benchmark for studying language change and retrieval in the 19th-century fiction and non-fiction. Our approach combines expert-driven query design, paragraph-level relevance annotation, and Large Language Model (LLM) assistance to create a scalable evaluation framework grounded in human expertise. Central to our investigation is knowledge transfer from fiction to non-fiction, examining how narrative understanding and semantic richness in fiction can enhance retrieval performance for scholarly and factual materials. This interdisciplinary framework not only improves retrieval accuracy but also fosters interpretability, transparency, and cultural inclusivity in digital archives. Our work provides both practical evaluation resources and a methodological paradigm for developing retrieval systems that support richer, historically aware engagement with digital archives, ultimately working towards more emancipatory knowledge infrastructures.
