Zuczkowski et al.’s KUB model clarified three epistemic stances: Knowing/Certain, Not Knowing Whether and Believing/Uncertain, and Unknowing/Neither Certain nor Uncertain, according to the speakers’ communicated information, and delineated three types of markers: macro-markers, micro-markers, and morphosyntactic markers. The model has seldom been applied to L2 instruction. To address this gap, the study examines the effect of grade and genre-specific questions on Chinese L2 speakers’ choice of epistemic markers with reference to the model by analyzing the self-built corpus consisting of the oral data collected from two groups: Group One consisting of 20 sophomores and Group Two comprising 20 first-year graduate students. The participants were required to answer four genre-specific questions covering argumentation, description, narration, and exposition. The results show that the two group members use similar epistemic markers (EMs) for the Knowing/Certain and Not Knowing Whether and Believing /Uncertain positions but present a slight discrepancy in Unknowing/Neither Certain nor Uncertain stance-taking. The genre-based questions demonstrate a significant effect on the graduate speakers’ use of the micro-markers and morphosyntactic markers for the Not Knowing Whether and Believing/Uncertain and the macro-markers and morphosyntactic markers for the Unknowing/Neither Certain nor Uncertain. It indicates that high-grade speakers are more sensitive to genre-based messages, though they use rather limited epistemic forms as low-grade speakers do. The findings suggest that English as a Second Language (ESL) oral instruction in China should be reformed and supplemented with diverse EMs to allow the speakers to take the epistemic stance they are comfortable with.

The study examined intellectual domains of meta-analysis used in SLA research based on Chinese core journals and English peer-reviewed journals from 2000 to 2022. Two datasets were created via CSSCI and Web of Science (WoS): 1) a Chinese dataset of core Linguistic and educational journals comprising 50 articles, and 2) a WoS dataset of 167 articles published in the reputed journals within registers of Linguistics and educational research. It employed document co-citation analysis (DCA) and author co-citation analysis (ACA) to capture the underlying intellectual structure and characterize distinct clusters in the WoS dataset. Co-word analysis and burst detection were adopted to discern the research trends, frontiers, and hotspots displayed in the two datasets. Results show that the WoS dataset focuses on the research topics: L2 instruction, corrective feedback, computer-mediated communication, self-motivation, and L2 writing with papers about procedures or amendments to meta-analysis as the knowledge base. The author intellectual groups categorized under specific contexts demonstrate a profound influence. Keywords captured in the WoS dataset can be mainly grouped into three types: research subjects, research topics, and terminologies relevant to meta-analysis. In contrast, research topics and terms related to meta-analysis are the dominant lexical chunks in the Chinese dataset. The emerging research spots, individual factors, language achievements, and knowledge maps provide directions for future research.