This study analyzes the distribution of novelty in Chinese Library and Information Science (LIS) papers published between 2000 and 2022, examining trends across journals, topics, and time periods. Using BERTopic for topic identification and combinatorial innovation theory for novelty scoring, the research investigates how collaboration patterns influence scholarly innovation.
- Archival research topics generally exhibit lower novelty, while journal evaluation and patent technology topics display higher novelty.
- The overall novelty of LIS papers in China has gradually increased over the analyzed period.
- Low-novelty topics are more frequently associated with solo authorship, whereas high-novelty topics tend to involve a higher proportion of inter-institutional collaboration.
This analysis provides new insights into how specific research topics and collaboration structures drive scholarly innovation within the Chinese LIS field.