Exploring cross-cultural narratives in literary studies: tradition, subjectivity, and strategy: an interview with Xiuyan Fu and Marco Caracciolo
Bibliographic record
Abstract
Abstract This interview article explores the dynamic intersections of cross-cultural narratology, focusing on three key dimensions: narrative tradition, narrative subjectivity, and narrative strategy. Through a conversation with two distinguished scholars – Professor Xiuyan Fu and Professor Marco Caracciolo – this discussion bridges Eastern and Western literary narrative traditions, delving into how cultural contexts shape narrative theories, how non-human entities contribute to the construction of narrative subjectivity, and how the concept of slow narrative functions as a narrative strategy in shaping modern literary narrative theory. The selection of these two interviewees is of considerable academic importance. Professor Xiuyan Fu is a leading figure in the field of Chinese narratology, renowned for his in-depth research on the narrative structures of traditional Chinese literature and their contemporary significance. His work not only provides an essential perspective on how cultural traditions shape narrative forms but also highlights the unique contributions of Chinese narratology within global narrative studies. Professor Marco Caracciolo is one of the foremost scholars in cognitive narratology and materiality studies in the West. His research has had a significant impact on narrative experience, embodied cognition, and environmental storytelling. He particularly examines how narrative engages readers’ embodied experiences and how non-human perspectives challenge anthropocentric narratives in the Anthropocene. His work not only expands the boundaries of narratology but also advances academic discussions in posthumanism and ecocriticism, offering new theoretical frameworks for narrative studies in contemporary interdisciplinary literature. By engaging in this dialogue, the interview aims to provide fresh insights into the future development of literary theory, particularly at the intersection of ecocriticism, narratology, and the digital humanities. The perspectives of these two scholars will contribute to these evolving academic fields while offering valuable guidance and reflections for emerging scholars.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.010 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".