The Culture Behind Writing: An Inquiry Into the Challenges and Cultural Influences on Second Language Writing in The Canadian Academic Context
Bibliographic record
Abstract
The current number of international students in post-secondary education in Canada is 388,782, of which 39.4% are in colleges, and 60.5% are in universities (TEST, 2022). International students bring multiculturalism, linguistic diversity, and varieties of English to Canada. They also come with specific and individual academic English (L2) writing skills and needs. To facilitate this bright and growing community, it is important to support them, particularly with L2 writing. There is a dearth of research on the L2 writing challenges faced by multilingual international undergraduate students (MIUS) keeping their multiculturalism and linguistic diversity in mind. Framed by the theories of Contrastive Rhetoric, Intercultural Rhetoric, Critical Contrastive Rhetoric, and Glocalization of English Language Use, this study explored MIUS’ L2 writing challenges, the influences of L1 culture, and their required support. The research questions were: What are the academic L2 writing challenges faced by the MIUS? What constitutes the cultures of the MIUS, and how do they influence their academic L2 writing? What strategies help to alleviate the academic L2 writing challenges of the MIUS, and how are they modified by the L1 of MIUS? What can educators, instructors and writing consultants do to provide better academic L2 writing support to the MIUS? Adopting a qualitative phenomenological inquiry design to conduct a multi-perspective exploration of the MIUS’ L2 writing experiences, this study explored the crucially important yet less explored voices of English language instructors, writing centre consultants and the MIUS. Two focus group discussions and 12 semi-structured interviews helped identify MIUS’ challenges with L2 writing mechanics, strategic language use, positive use of MIUS’ L1 in L2 writing process, the effective L2 writing strategies, and the need for educators and writing centre consultants to be more aware of MIUS’ L1 and the varieties of English. The findings contribute to theory by re-defining the scope of the MIUS’ individual cultures and introducing prior English language education as a factor impacting their L2 writing. The study also offers future research recommendations to further enhance the findings of this study and for new studies on how best to support the academic writing success of the MIUS.
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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 | 0.009 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.047 | 0.020 |
| Scholarly communication | 0.019 | 0.004 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| 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".