Thematic Tendencies in Heteronymous Ci-Tune Titles Sharing the Niannu Jiao Prosodic Pattern
Keywords:
Classical Chinese Poetry, Niannu Jiao, Thematic Distribution, Ci-Tune Title Studies, Chinese literatureAbstract
This study investigates the thematic selection tendencies in the original and heteronymous forms of the Niannu Jiao ci-tune title. It not only contributes to understanding the flexible adoption of tune-title variants by classical Chinese poets across diverse themes but also reveals the evolution of topic selection and its underlying cultural determinants, providing theoretical foundations and creative insights for contemporary classical-style poetry composition. Based on traditional literary theory and employing modern scientific research methods, this study explores both the synchronic and diachronic dimensions of thematic distribution in the Niannu Jiao tune title and its variants, aiming to elucidate the relationship between thematic content, tune-title nomenclature, and changing literary trends across historical periods. Drawing on a literature review and statistical analysis of 2,205 extant ci poems composed under the original Niannu Jiao title and its 18 heteronymous variants, with metadata on authorship and chronological distribution, the study classifies each work according to 35 thematic categories and conducts quantitative comparisons of their dynastic distribution. The findings reveal distinct thematic preferences: the original Niannu Jiao title is mainly associated with topics of object depiction, celebration, and social interaction, whereas several heteronymous titles tend toward landscape and religious themes. These tendencies correlate closely with the semantic connotations of the tune titles and show observable variation over time. The research scope is limited to extant and verifiable works, and the small sample size of certain heteronymous titles may affect statistical precision. Additionally, subjective interpretation and the complexity of genre classification present methodological constraints. Future research may employ multi-topic modeling to achieve deeper thematic analysis.
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