Challenges Facing Jordanian EFL Translation Students When Translating Literary Texts
Bibliographic record
Abstract
The goal of this research is to look into literary text translation. The purpose of this study is to discover the challenging elements of literary texts among translators of English translation specialists at Jordanian Universities in translating a literary text. This study intends to uncover the most prevalent errors made by students while translating literary texts, as well as to compare how these students translate metaphor and other figures of speech. The qualitative research design was used by the researcher to attain the study's purpose. The participants in this study were 20 translation students from different Jordanian universities. The study found that one of the biggest obstacles for literary translators is that they do not have a strong understanding of both languages' literature. This research found that the most common errors were directly tied to the employment of translation techniques in literary texts. Furthermore, students of translation were missing one of the key features. They lack a sense of literary translation. According to the findings, translation departments should focus on teaching methodologies and approaches for translating literary materials. It also suggests that a study be conducted to compare the differences in translation between translation specialists and linguistic experts. In fact, this new contrast could bring up some fresh thoughts.
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".