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Record W1756407074 · doi:10.5539/ies.v8n8p67

Enhancing Intercultural Communication and Understanding: Team Translation Project as a Student Engagement Learning Approach

2015· article· en· W1756407074 on OpenAlexvenueno aff
Yang Ping

Bibliographic record

VenueInternational Education Studies · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Practises and Engagement
Canadian institutionsnot available
FundersAustralia-China CouncilDepartment of Foreign Affairs and Trade, Australian Government
KeywordsIntercultural communicationCompetence (human resources)PedagogyPsychologyAutonomySocial constructivismCultural competenceIntercultural competenceTarget cultureSociologySocial psychologyPolitical science

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.495
Threshold uncertainty score0.568

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.415
GPT teacher head0.512
Teacher spread0.097 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2015
Admission routes1
Has abstractyes

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