Strategic Competence of Bilingual Undergraduate Engineers in a Technical University
Bibliographic record
Abstract
Today’s increasingly borderless, transcultural and challenging business world, requires engineers to becommunicatively competent in the oral and written form. Unfortunately, breakdowns in communication oftenhappen, due to linguistic and psychological boundaries. In such conditions, verbal and non-verbalcommunication strategies also known as strategic competence helps to compensate the breakdowns. This studyseeks to determine whether the Malay bilingual engineering undergraduates, who form the majority in atechnical university in Malaysia, adopt the avoidance or achievement strategy dominantly in attaining a writtencommunication goal. The instruments that were used in the study include survey questionnaires, focused groupinterview and Written Discourse Completion Tasks. The findings based on the Written Discourse CompletionTasks reveal that the achievement strategy which includes literal translation manifests as the most dominantstrategy employed by the undergraduates. The paper concludes with a note on the significant role played bylanguage instructors in providing optimal scaffolding. It also points towards directions for future inquiry on theneed for a rigorous review of the language curriculum in technical universities so that the undergraduateengineers are able to improve their strategic competence as well as overcome the momentary inadequacy of theirsecond language resources due to the linguistic repertoire of the Malay language which stigmatizes them asspeakers of the second language.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".