Use of Google Translate for Translating Scientific Texts: An Investigation with Saudi English-Major Students
Bibliographic record
Abstract
English is not the first language in Saudi Arabia, which makes most students face problems with the most used language globally, consequently pushing most Saudis to use the Google Translate (GT) application. This paper reports the attitudes and perceptions of Saudi EFL students on using Google Translate, the strategies applied, the number of editions they make, and the reasons for amending the Google Translate outputs. The study sample comprised 43 English major students at Qassim University, Saudi Arabia. A validated questionnaire was used for data collection, followed by translation tasks where the participants would do translations between English and Arabic languages. Results showed that most of the participants frequently used GT in their English learning. The results also indicated that Saudi EFL students frequently edited the syntax produced by GT in addition to checking the meaning of some new words. The study concludes with some recommendations, most importantly that Saudi EFL students should be motivated and encouraged to make use of GT to conserve their time; however, total dependency is not welcome.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".