English-as-an-Additional-Language Employees’ Perspectives on Writing in the Workplace
Bibliographic record
Abstract
This article presents study results on workplace writing from English-as-an-additional-language (EAL) employees’ perspectives, and shares findings about how educational institutions in British Columbia can better prepare EAL students to write in the workplace. In post-secondary academic writing, content rather than writing accuracy is often emphasized, yet most employers consider writing accuracy important as it reflects a company’s image (Hu & Hoare, 2017), and how EAL employees perceive their writing preparedness, workplace writing accuracy, and language challenges remains unexplored. Thus, we inquired: 1) How do EAL employees graduated from English-speaking universities and working in English-medium environments perceive workplace writing accuracy? 2) To what extent are they prepared for workplace writing? 3) What writing challenges do they encounter? 4) What do they think universities can do to better prepare EAL students for workplace writing? The study employed qualitative interviews with nine EAL employees who graduated from British Columbia universities and were working at English-medium companies in Canada. Data analysis suggests that the participants highly valued writing accuracy; however, their education did not prepare them adequately. In addition, the participants suggested that universities offer more communication, business, and professional writing courses; enhance support services; invite employers and EAL employees as guest speakers; and incorporate real-life scenarios in the curriculum.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.009 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".