Bibliographic record
Abstract
This study aims to highlight errors in translating Arabic phrases and expressions into English. It is part of a research that attempts to establish some cultural connections between those translational mistakes and the embedded Arabic and Saudi religious and cultural factors that influence making such errors. To achieve the set goal, the researcher observed many written English signs around the city of Tabuk in a period of two years and then archived and analyzed the various translation mistakes collected from universities’ announcements, religious flyers, hospital signs, bill board signs, shops and malls signs, personal signs...etc. The errors were classified into four categories: Singular/Plural, Sentence Structure and Syntax, Word Choice, and Spelling errors. Then a quiz was given to selected female English major students at the University of Tabuk; the quiz contained the same observed mistakes collected earlier. Therefore, the sample of the study was very diverse in its nature of Saudi Arabs and Arabs from other Arab countries that came to live and work in the city of Tabuk; while the students who took the quiz were all of Saudi nationality. It was concluded that the reasons Arab people who publish English translations fail to transfer the Arabic equivalence of English phrases and expressions are mainly due to literal translation and influencing cultural factors that make those people unfamiliar with the use of the right English words in their proper context.
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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".