Developing Mutual Intelligibility and Conviviality in the 21st Century Classroom: Insights from English as a Lingua Franca and Intercultural Communication
Bibliographic record
Abstract
& Due to ongoing increases in global mobility and migration, global citizens consistently find themselves in contact with a range of linguistic and cultural backgrounds (Zhu, 2011).Through a growing focus on multilingualism in second language (L2) research (i.e., Ortega, 2013), greater recognition has been given to the complex multilingual repertoires of language users across the globe (Blommaert, 2010).However, a primarily pedagogical focus on the linguistic components of interaction overlooks the role that cultural differences may play in mis-and nonunderstandings between speakers (Scollon, Scollon, & Jones, 2012).Blommaert (2013) prioritized a recognition of complexity over multiplicity and plurality when considering the cultural components of global contact, arguing that current investigations are limited to individual zones of contact, without consideration of how what begin as distinct cultural units are transformed and carried over into subsequent global contact.As such, Blommaert is following up on Leung's (2005) argument for conviviality between global English users, where linguistic and cultural distinctions are not immediately evaluated due
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.026 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".