ELF and Transcultural Communicative Practices in Multilingual and Multicultural Settings: A Theoretical Appraisal of Recent Advances
Bibliographic record
Abstract
Transcultural communicative practices (TCPs) have become increasingly important in English language pedagogy owing to the growing number of multilingual and multicultural settings. In this study, I theoretically appraise these practices and place them in the context of English as a lingua franca (ELF) and transcultural communication in the English language classroom. Drawing on Takkula et al.’s (2008) claim that all people are products of their native culture and mother tongue from the moment of birth, the paper argues that language students must be educated to overcome their culture-bound assumptions and beliefs and acquire the necessary skills for transcultural exchanges and that to this end, ELF is a valuable transcultural communication tool. The benefits and challenges of such practices are also discussed. The benefits for students include increased cultural sensitivity, improved communication skills, and enhanced critical thinking, whereas the challenges include language barriers, cultural differences, and class time constraints. The study concludes with hints for English language teachers and practitioners to effectively implement TCPs in classrooms to promote transcultural understanding and communication among students.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| 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".