The Ability of Translation Students to Translate Environmental Expressions at Jadara University in Jordan
Bibliographic record
Abstract
This paper has coped with Environmental Sentences and Expressions (ESE). It aims at investigating the ability of translation students in translating the ESE at Jadara University in Jordan. Based on a practical study, a sample of 20 translation students was chosen randomly from the English Department, nine of them are urban, while eleven students are rural. For achieving the purpose of this paper and to collect data, a test of 25 environmental items was set up; the validity and the reliability were verified by a panel of judges at Jadara and Yarmouk Universities. Quantitatively, the researcher used SPSS to analyze data. Frequencies and percentages as a statistic method used to examine students’ ability in translating environmental terms and expressions. To know the differences between urban and rural students, T-test was used as another statistic method. The results of the study showed a poor level in translating environmental expressions. The results also revealed no significant differences between urban and rural students. In light of the study’s findings, it was recommended for issuing environmental course as an elective course for English students, and as a compulsory for translation students. Finally, some suggestions for further researches were written.
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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 | 0.004 | 0.009 |
| 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 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".