The Impact of Pragmatic Markers Acquisition and Phonological Awareness on Word Choice in Translating Literary Texts from Arabic into English
Bibliographic record
Abstract
Pragmatic markers, either primary or secondary, contribute to the specificity of languages and are sensitive on being translated. This study traces the use of well, the commonest pragmatic marker in the English discourse, in a corpus of translated Arabic novels. The study, too, addresses the influence of the translators’ phonological awareness on their word choices, in the same corpus. Adjacent consonants, consonant-starting and quarter-syllabic words are studied in four groups: free writing of native authors as a control group (G1), literary translations by native English translators (G2), literary English translations by Arabic translators (G3) as well as literary English translations by joint effort (native-speaking and non-native-speaking translators) [G4]. The findings are statistically compared using one-way ANOVA test. Results show a statistically significant difference in the use of the pragmatic marker well and in the use of the three phonological patterns among the four groups. The findings are interpreted and implications are offered for the pragmatic gap and linguistic competence between native-speaking and non-native-speaking translators.
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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 | 0.001 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".