Cultural Linguistics and Intercultural Communication
Bibliographic record
Abstract
The international classroom is rapidly becoming the norm rather than the exception in most academic settings in North America and throughout the world. This internationalization also implies the co-presence of a variety of cultural backgrounds. At most Canadian universities, many classes have a very mixed student body, with many international students or recent immigrants to the country for whom English is not the first or even the second language (cf. Cecchetto and Stroińska 2006 ). In the last few years, electronically mediated forms of communication (CMCs) have replaced more traditional face-to-face exchanges between instructors and students and among the students themselves outside the classroom. This change in the medium and channel of communication naturally influences the dynamics of human interaction. Because of this shift, any principles of facework originally developed for the once natural face-to-face encounters (cf. Goffman 1967 ) need to be modified and adapted to the new means of communication. The fact that in CMC the addressee may not be aware of the cultural background and the native versus nonnative speaker’s status of the sender may result in the addressee possibly forming opinions and impressions about the sender without the benefit of the information available in the face-to-face encounters. In this chapter, we focus on e-mails initiated by students and comment about the effects of the language and politeness strategies used (or lacking) in order to examine why some e-mail formats may appear impolite or rude to the instructor and how this may inadvertently affect the student. Even though CMC seems to offer a level playing field for nonnative speakers of the language, we focus on situations where the difference in cultural backgrounds may play a role in the perceptions of nonnative speakers and in negative stereotyping.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.007 | 0.032 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".