CULTURAL FUSION, CONFLICT, AND PRESERVATION: EXPRESSIVE STYLES AMONG
Bibliographic record
Abstract
In this study, multicultural literature served as a site for Chinese Canadians to explore the interplay between their dual cultural backgrounds. After reading a story written by a Chinese Canadian author, participants were invited to imagine a dialogue between two characters with whom they identified, allowing the exploration of different aspects of their bicultural selves. Sys- tematic examination of their dialogues, using cluster analysis of recurrently expressed dialogical themes, revealed four distinct expressive styles (Rhetorical Conflict, Imperative Conflict, Active Narration, and Embodied Reconciliation), each revealing a different bicultural stance. Both the rhetorically probing and explicitly imperative styles of expression reflected a fusion of Chinese and Canadian expectations regarding confrontation, although in different ways each also facili- tated the maintenance of a conflictual cultural hierarchy. Active, but distanced, narrative descrip- tion reflected the preservation of a collective sense of self that is characteristic of traditional Chi- nese culture. Finally, dialogic enactment of conflicting voices allowed reconciliatory, embod- ied, and generative fusions of Chinese and Canadian cultural expectations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".