Saudi Phatic Communication in Translation: A Cultural and Linguistic Perspective
Bibliographic record
Abstract
Adopting a functional approach to translation, this study dives deep into phatic communion expressions, categorizing them in relation to their direct translations in English and identifying culturally equivalent phrases in manual and machine translation. With a newly developed corpus of 157 Saudi Arabic phatic expressions, the study classifies them into eight categories, viz., greetings and rituals, politeness, inquiries about well-being, blessings and good wishes, small talk, acknowledgement and agreement, farewells and departure, and expressions of gratitude and appreciation. The corpus is created from five Arabic language films classified as popular choices on Netflix. The corpus is then translated by 21 final year English Language program students at Shaqra University, KSA, and by Google Translate (GT). Findings show that most frequently phatic expressions are used to express polite and warm introductions, maintaining courteous communication, and for cultural and religious dimensions. Findings also indicate that the Saudi students demonstrated a high degree of communicative translation of the Arabic phatic expressions into English whereas GT output was off the mark and even irrelevant in many instances, making a case for discouraging the use of GT in Saudi translation studies classrooms. The study concludes with pertinent recommendations offering insights that can be useful in fostering understanding and cultural sensitivity among non-Saudis interacting with Saudi individuals, making this study crucial for those in the fields of translation, intercultural communication, and linguistics.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".