Culture and L2 writing
Bibliographic record
Abstract
Culture in second language (L2) writing has been researched extensively, though mostly under the purview of contrastive rhetoric and focused on text and contrastive genre analysis (Connor, 1996, 2004, 2008; Kaplan, 2005). Research has also focused on problematizing culture in reference to L2 writing (Atkinson, 1999, 2003; Kubota, 1999). These foci indicate reader-instructor rather than student perspectives: how L2 writers themselves perceive cultural impacts on writing. This study undertakes to fill this gap, investigating L2 student perceptions of such impacts. Study participants (n = 36), students in an English for Academic Purposes (EAP) writing course at a Canadian university, took part in semistructured interviews and reflective writing. Data analysis identified six broad categories of cultural factors affecting student writing: (1) organizational structure as a fixed method; (2) supporting and writing arguments; (3) creating a stronger voice in writing; (4) adjusting to a new academic culture; (5) understanding clarity in academic writing in English; and (6) developing content: quality versus quantity. Findings underscore student perceptions of a monolithic, essentialist view of culture in academic writing. This is an important consideration when designing a student-centred L2 writing pedagogy that addresses student needs. Based on the findings, the article further explores implications for L2 writing instruction.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".