Saudi Translation Agencies and Translation Centers: A Study of Post-Editing Practices
Bibliographic record
Abstract
This qualitative research aims to investigate and analyze the practice of post-editing activity in Saudi translation agencies and translation centers. The study’s focus is on the importance of post-editing Machine Translation (MT) output as an approach to upgrade the quality of MT results to enhance the accuracy of translations. The study data were collected through an electronic survey (questionnaire) comprising short questions about post-editing practice and guidelines in addition to their knowledge about post-editing certification programs. The participants in this study were 18 professional translators working in certified translation agencies and translation centers across different universities between Jeddah, Dammam, and Riyadh cities. The results of the study showed that the participants post-edited the MT outputs in their tasks and recognized the importance of being aware of post-editing standards and guidelines. Further, the findings demonstrated that the majority of the participants used machine translation tools. Finally, they were positively disposed about post-editing certification programs to take employment as post-editors. Based on the results of this study, it is recommended to analyze the state of post-editing in Saudi Arabia on a larger sample to investigate the attitudes of Saudi translation students towards practicing post-editing of MT outputs, and analyzing the activity of post-editing MT in specialized texts by Saudi professional 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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".