Enhancing Translation Students’ Intercultural Competence: Affordances of Online Transnational Collaboration
Bibliographic record
Abstract
Intercultural communication has evolved considerably throughout the digital era thanks to the availability and diversity of virtual communication platforms. Few research studies have explored the potential role of digital spaces in teaching translation courses. This paper reports an empirical study that explores the affordances of online transnational collaboration in reconstructing the identities of translation students. The study tested the potential of telecollaboration in teaching a course on translating literature with particular focus on translating for children. The total number of participants was 28 students from an Omani university. The participants completed translation tasks while involved in international telecollaborative projects with students from an American University. The study adopted the action research method with data collected from the students’ translation tasks and reflections before, during, and after telecollaboration. The findings showed that the participants’ intercultural competence developed following telecollaboration with their counterparts. The students’ translation approach shifted from TT-oriented to ST-oriented with translation strategies that prioritized the cultural paradigms of the other culture. The students embraced the concept of global citizenship and achieved the course learning outcomes and graduate attributes of course syllabus. The results also highlighted the significance of online academic partnerships in improving translation students’ intercultural communication skills.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".