Use of English in engineering workplace: Frequency, skill hierarchy, and functions
Bibliographic record
Abstract
The existing body of research on workplace English for engineers highlights the significance of English in the engineering work. However, there is limited understanding of the use of English in the engineering profession in terms of the frequency of English use, the relative importance of macro skills such as speaking and writing, and the functions of the English language. This study examined diaries written by 97 in-service mechanical engineers regarding their experiences with English language use in their workplaces. Follow-up interviews were conducted with a sub-sample for triangulation. A summative content analysis of the diary data was carried out to identify the frequency of the use of English and the hierarchy of the use of the four skills (i.e., reading, writing, listening, speaking). In addition, thematic analysis and summative content analysis were employed to identify the functions of English in the engineering workplace. Findings reveal that the majority of the participants used English on 21–25 days during the 30-day data collection period. ‘Writing’ is the most frequently used skill, and ‘informative’ is the dominant function. Besides, in the engineering workplace, English primarily serves transactional functions while interactional uses remain limited. We conclude with implications for language educators and policymakers in relation to the use of the English language in the engineering sector.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".