A Ground-Truth Dataset for Article Separation in Historical Newspapers: A 東方雜誌 corpus centered on 中華教育文化基金會 (1924–1947)

This ground-truth dataset contains carefully curated and properly segmented documents derived from an original corpus of news articles focused on China Foundation for the Promotion of Education and Culture 中華教育文化基金會 (established in 1924 to manage the Second remission of the U.S. Boxer Indemnity. The dataset includes 108 articles published in the 東方雜誌 journal (The Eastern Miscellany, 1904-1948) between 1924 and 1947. The ground-truth data contains the following fields: DocId: Unique identifier as stored in the Modern China Textual Database (MCTB). Year: year of publication Volume, Issue Title: Title as provided by the data supplier authors: author(s) of the article category, category_strd, category_sup: article category, as given in the original source (category), and normalized by historians (2 levels of granularity) Text: Original, unsegmented text text_seg: Historian-curated segmented text, produced using GPT + close reading length: Character/word length of the original text length_seg: Character/word length after re-segmentation diff: length difference between original and segmented text The segmentation process uses a hybrid human–AI workflow: an automated step with a GPT-based “Historical Text Segmenter,” followed by detailed historian-guided verification and correction. The result is a high-quality ground-truth dataset suitable for OCR benchmarking, segmentation modeling, historical text analysis, and digital humanities research. Additional documentation on the configuration of the GPT “Historical Text Segmenter” is available here. Use Cases This dataset is intended for: Historical research on academic institution and Sino-American cultural relations Media and discourse analysis of 東方雜誌 Training/evaluating segmentation models Digital humanities projects requiring high-quality ground truth corpora Studies of textual reuse and viral news circulation in Republican-era periodicals