A Linguistic Analysis on Errors Committed by Jordanian EFL Undergraduate Students: A Case of News Headlines in Jordanian Newspapers
Bibliographic record
Abstract
This study investigated some linguistic errors committed by Jordanian EFL undergraduate students when translating news headlines in Jordanian newspapers from Arabic to English and vice versa. The data of the study was collected through a test composed of (30) English news headlines and (30) Arabic ones covering various areas of news occurring in a large corpus of Jordanian newspapers, i.e., two leading and prominent newspapers were selected. The test was administrated to a randomly selected sample consisting of (40 female, 20 male) third and fourth year undergraduate students in the Department of English Language and Literature in the Faculty of Educational Sciences and Arts at UNRWA University in Amman, Jordan. Results from the first analysis of the translated Arabic news headlines indicated that the EFL students had grammatical and lexical errors respectively. The second analysis of the translated Arabic news headlines showed that the EFL students had inadequate knowledge of the English headlines rules. The analysis of the translated English headlines revealed that the EFL students’ main difficulties were grammatical followed by discoursal and lexical types. In light of these results, the researcher proposes a number of pedagogical recommendations related to translating news headlines and future research.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".