Enhancing Intercultural Communication and Understanding: Team Translation Project as a Student Engagement Learning Approach
Bibliographic record
Abstract
This paper reflects on a team translation project on Aboriginal culture designed to enhance university students’ intercultural communication competence and understanding through engaging in an interactive team translation project funded by the Australia-China Council. A selected group of Chinese speaking translation students participated in the project and two English books on Australian Aboriginal history and culture were translated to Chinese from August 2011 to May 2012. The two bilingual books were published by Aboriginal Studies Press in May 2013. After the one-year translation project was completed, the author conducted a survey and audio-taped interviews about the participants’ translation experience. Using social constructivist theory (SCT), the author coded the data, conducted critical analysis of the contents, and categorised the themes. It was found that the participants not only improved their translation skills through combining theories with practices, but also got better knowledge of Australian Aboriginal cultural tradition and history than before. Having understood cross-linguistic differences, they combined translation theory with practice and raised their intercultural awareness after going through various organized learning activities centring on the translation project. Such an interaction-based student engagement learning approach helped student translators achieve meaningful communication and learner autonomy through individual reflections, group discussions, and seminars. Finally the pedagogical implications of the team translation project were discussed.
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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 | 0.022 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.003 | 0.005 |
| 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".