The Effect of Transitivity, Futurity, and Aspectuality on the Translation of English Present Progressive into Arabic Verbal and Active Participle Counterparts
Bibliographic record
Abstract
Arabic lacks a specific form for progressive tenses and instead uses the imperfective form ‘jafʕal’ to express habitual and progressive aspects. Arabic also uses an active participle form (AP) to express progressiveness. This paper addresses the effect of transitivity, futurity, and aspectuality on the translation of English present progressive (PP) into Arabic verbal and active participle counterparts. To investigate which of the two forms is used to translate English PP into Arabic, data were collected from 100 students who were studying an elective ‘translation’ course at Princess Sumaya University for Technology (PSUT). The researchers built a questionnaire of 38 English sentences each of which has two main translations: one that uses the imperfective form ‘ja-fʕal’ and another that has an (AP) form, mainly ‘fa:ʕil’ or ‘mu-fʕil’. The participants were asked to rate the acceptability of each sentence on a scale of 0-2. The findings reveal that transitivity and the future reading of the progressive verb affect the translatability of the progressive tenses as imperfective or (AP) form. Transitive verbs are more likely to be translated as imperfective verbs than transitive APs because (AP) does not have as strong verbal properties as lexical verbs. On the other hand, translocative verbs accept (AP) translations fairly enough to refer to future. The findings also reveal that the aspectuality of the verb affects its translation in one of the two main forms mentioned above. (AP) translations of English (PP) become more acceptable when the root of the verb indicates state-of-affair actions, achievements or accomplishments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".