Chinese-as-a-First-Language (CL1) and English-as-a-First-Language (EL1) Undergraduate Students' Business Writing in Canadian Universities: A Corpus-Based Contrastive Study of Linguistic Features
Bibliographic record
Abstract
The importance of formulaic language, such as Lexical bundles (LBs) (e.g., as a result of, the value of the), the introductory it patterns (e.g., it is important to), and self-mention markers (e.g., I, me, you) in academic writing have been well recognized (Guan, 2022; Hyland, 2002b, 2005; Larsson, 2017). Those linguistic patterns are essential for organizing texts, constructing writers’ arguments, and projecting their voices in academic prose (Güngör, 2019; Hyland, 2002; Zhang, 2015). Nevertheless, the investigation of LBs, the introductory it patterns, and self-mention markers used by English-as-an-additional-language (EAL) undergraduates is limited. \nIn Canada, the number of Chinese-as-a-first-language (CL1) undergraduate students in the business major has increased significantly (CBIE, 2022). Given that LBs, introductory it patterns, and self-mention markers are challenging for CL1 students (Leedham, 2011), it is crucial for researchers and practitioners to understand those linguistics features used by CL1 students in the business discipline compared to English-as-a-first-language (EL1) students. Through comparative analysis, this study aims to provide greater insights into the structural and functional uses of LBs, the introductory it patterns, and self-mention markers used by CL1 and EL1 business students. \nSpecifically, the current study aims to fill the gap by analyzing the most frequent 4-word LBs, the introductory it patterns, and self-mention markers in CL1 and EL1 undergraduate students’ business writing concerning the frequency, structures, and functions of those linguistic features. The two self-compiled corpora, EL1 corpus, and CL1 corpus, including 42 articles in each corpus, were collected from second-year university-level business writing courses. Those linguistic patterns were analyzed quantitatively and qualitatively using the corpus analysis software AntConc (Anthony, 2023). \nThe results suggest that CL1 students showed significantly higher use and more variation of LBs and self-mention markers than EL1 students, while EL1 students employed significantly more instances with introductory it patterns. Regarding LBs, the use of LBs in EL1 and CL1 writing was similar, with a heavy reliance on verb-based phrases, indicating undergraduate students’ writing style as immature learner writing (Chen & Baker, 2010, 2016). With respect to the introductory it patterns, the introductory it has two prominent interpersonal roles in stance marking and interpreting observations. The main differences between the two corpora are in using the introductory it to hedge a claim and emphasize the writer’s attitude, with CL1 students making fewer hedges and overt persuasive statements. Concerning self-mention markers, the first-person pronoun I was the most frequent self-mention marker, followed by we in both corpora. The functions of self-mention markers used by both groups are primarily associated with low-risk functions, including expressing self-benefits and explaining procedures. Since limited uses of those linguistic patterns were identified in both corpora, the findings suggest pedagogical implications for teaching LBs, introductory it patterns, and self-mention markers in the business writing curriculum for CL1 and EL1 undergraduates.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| 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 teacher head, 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".