The Translational Force of Words: The Journey of “Come” and “Go” into Arabic
Bibliographic record
Abstract
This study delves into the translational challenges Arabic translators face in preserving the dynamism and multifaceted meanings of ‘come’ and ‘go’ phrasal verbs during translation using machine tools. Though existing literature has focused on linguistic and cultural equivalence, the ‘translational force’ of specific words is still under-researched. The aim of this study is to verify the accuracy of the output across tools, and to conclude how effective these applications were in the given language pair. The study adopts a mixed methods approach with a survey used to identify the most frequently used machine tools for translation by 27 learners of Translation Studies at Shaqra University, Saudi Arabia. Thereafter, a set of frequently used phrasal verbs in English that use the root verbs ‘come’ and ‘go’ were translated by a convenience sample of 27 final year students of translation as well as by using the popular and free translation tools in the English-Arabic language pair. Results indicated that Saudi students enrolled in translation studies used MateCAT, Google Translate, Reverso to a limited extent, but relied more on online dictionaries for translation to and from English. In addition to this, translation of English phrasal verbs was found to be most accurate in MateCat and there too, accuracy in back translation was found to be more accurate in the case of the phrasal verbs formed with ‘come’ at the root than with ‘go’ verb. Finally, it was found that the translation students preferred ‘soft tools’ like English movies for learning, recall, and use of English phrasal verbs in practice. This study is expected to provide a deeper understanding of the concept of "translational force" and its implications for translating dynamic words in the English-Arabic language pair in the realm of machine-based translation tools.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".