The Status Quo of Translation Technology Tools in Translator Training Programs at Jordanian Universities
Bibliographic record
Abstract
This quantitative study investigates the status quo of integrating translation technology tools in translator training programs at Jordanian universities. Adopting a descriptive approach with a questionnaire, the study explores students’ perceptions of various aspects of translation technology tools, focusing on their adoption, ease of use, and impact on learning outcomes. The study sample consists of (400) translation students from the University of Jordan, Mutah University, and Al-Zaytoonah University of Jordan. The study tool is a structured Google Forms survey comprising several factors, addressing specific aspects of TT tools usage. The statistical analysis of the questionnaire data reveals that students generally have a positive perception of translation technology tools in terms of usability, future profession, and learning and development, which increase translator productivity. The findings also emphasize the need for universities to enhance the integration of translation technology tools training, address accessibility issues, and equip students for a technology-integrated translation profession. In addition, it is recommended that educational institutions continue to invest in translation technology tools. Furthermore, the study's implications are reflected in advising collaboration between academia and industry professionals to anticipate and predict future trends and ensure that students are adequately ready to work with emerging tools and workflows.
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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 | 0.055 | 0.074 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".