"Decoding the Socialist Business Genre: A Computational Reading of Mao-era China’s Industrial Enterprise Texts"

Computational text analytics, including topic modeling, dependency parsing, and natural language processing, open new avenues for analyzing large and complex bodies of text. This paper introduces a novel computational framework to examine the industrial business genre produced in Mao-era China, including factory gazetteers, industrial novels, and government annuals. Utilizing an original database of industrial enterprise texts developed through advanced OCR (Google Vision) and Handwritten Text Recognition (HTR) technologies, this study merges distant reading with close case studies to reveal patterns, biases, and perspectives embedded within these narratives. It investigates how political campaigns led by Mao Zedong shaped narratives and memories of Chinese industrialization and considers the role of business historical genre in reinterpreting the political and social transformations of the Mao era. By engaging computational reading in the study of Chinese business history, this research seeks to contribute a reflective and critical methodology to the historiography of socialism and business history broadly.