From Intercultural to Transcultural Communication: ELF in Multilingual Settings
Bibliographic record
Abstract
The study centres around the idea that English is a global lingua franca for intercultural communication among multilingual speakers. Drawing on relevant literature in the field, the paper will highlight the intercultural and transcultural nature of English used as a multilingua franca (EMF). In particular, the study will explore learners’ attitudes with the purpose to identify how learners perceive the complex relation between culture and language and the factors which may affect intercultural communication through English. The study will analyse two sample groups. The first group is composed of students belonging to different first language backgrounds, mainly non-native speakers of English studying in an Italian university. The second group is composed of both native and non-native English speakers studying in the US and Canada. An online link to a questionnaire was sent via email to all participants and was used as a research instrument to collect quantitative data. It is highlighted that English used in multilingual settings cannot be analysed as a static and bounded entity with precise boundaries. On the contrary, English has transcended boundaries, in a fluid, dynamic and flexible process where borders are fuzzy and blurred and languages do not reflect well-defined national cultures. Therefore, it is suggested that language teaching practices should incorporate intercultural/transcultural oriented issues to provide learners with a more comprehensive knowledge of the multifaceted global English world and encourage a richer cultural and linguistic experience.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".