The Human Touch: Leveraging HITL for Quantitative Close Reading of Historical Corpora

This paper introduces Quantitative Close Reading (QCR), a Human-in-the-Loop (HITL) methodology designed to structure and enrich noisy digitized historical corpora – particularly text archives compromised by poor Optical Character Recognition (OCR) quality. QCR integrates manual annotation with quantitative analysis, enabling researchers to trace semantic shifts and symbolic representations over time while preserving interpretive depth. We detail the methodological framework of QCR, including keyword selection, annotation manual development, coder training, and postprocessing techniques. Through three case studies that trace semantic shifts in the representations of fællessang [communal singing], the mythological figure Dana, and the Catholic shrine Lourdes, we demonstrate how QCR bridges close and distant reading, operationalizes conceptual history at scale, and transforms fragmented textual data into reliable, analyzable corpora. By combining the scalability of computational methods with the contextual sensitivity of human annotation, QCR offers a robust approach for historical semantic analysis across digitized texts.